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检索条件"任意字段=2024 3rd International Conference on Image Processing, Object Detection, and Tracking, IPODT 2024"

共 128 条记录，以下是61-70 订阅

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Vision-Guided Robotic System: image processing and Kinematic Integration 3rd

Vision-Guided Robotic System: Image Processing and Kinematic...

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3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

作者： Wan, Changcheng School of Geospatial Engineering and Science Sun Yat-sen University Zhuhai China

ISBN: (纸本)9789819791279

This paper explores the development and implementation of a sophisticated vision-guided robotic manipulation system, underpinning advancements in manufacturing, healthcare, and service robotics through the integration of image processing and kinematic control. The system employs a serial-link robotic arm equipped with a high-resolution camera, facilitating real-time environment interaction through precise object tracking and manipulation. Central to the research is the design of the robotic arm’s kinematic model, the application of advanced image processing for object detection and tracking, and the formulation of control strategies translating visual data into accurate manipulative actions. The study underscores the significance of merging image processing with robotic control to adapt dynamically to changing environments, leveraging techniques such as background subtraction, median filtering, and binary thresholding for efficient object isolation. The implementation of kinematic equations enables the calculation of the robotic arm’s movements to interact with objects accurately, considering spatial positioning and geometric constraints. Experimental results demonstrate the system’s efficacy in enhancing operational efficiency and flexibility in diverse application scenarios, achieving over 95% tracking accuracy and rapid response times. This research contributes to the robotic manipulation field, highlighting the potential of combining image processing and kinematic control to innovate robotic system designs, with broad implications for industrial automation and assistive technologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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Contextual Transformer Based Small Targets detection for Cervical Cell 3

Contextual Transformer Based Small Targets Detection for Cer...

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3rd international conference on image processing and Media Computing (ICIPMC)

作者： Yin, Juanjuan Zhang, Qian Lu, Wenjing Peng, Jinye Wang, Jun Li, Xiaohui Northwest Univ Sch Informat Sci & Technol Xian Peoples R China Northwest Univ Sch Informat Sci & Technol State Prov Joint Engn & Res Ctr Adv Networking & Xian Peoples R China Xian Shiyou Univ Coll Chem & Chem Engn Xian Peoples R China

ISBN: (纸本)9798350386660;9798350386677

Cervical cancer poses a significant threat to women's health as a malignant tumor. Currently, liquid-based thin-layer cytology stands as the predominant method for detecting cervical cancer cells. The conventional approach to identifying cervical cell pathology images relies heavily on skilled medical professionals, a process known for its time-consuming and labor-intensive nature, along with its inherent limitations. Although the integration of deep learning and imaging has demonstrated promising results in medical auxiliary diagnosis, existing methods for small object detection encounter challenges in effective feature extraction and the balanced representation of positive and negative samples. To address the aforementioned issues, we propose an optimized model for cervical cell detection that bolsters the model's capability for extracting features related to small targets by leveraging a fusion of context and self-attention mechanisms. Our approach employs an Adaptive Training Sample Selection (ATSS) strategy for Anchors to ensure a proportionate representation of positive and negative samples. Furthermore, we incorporate a softer Soft-NMS algorithm to prevent the abrupt elimination of small targets and utilize Focal Loss to steer the model towards prioritizing small target identification. Experimental evaluations conducted on a publicly accessible cervical cell dataset validate the efficacy of our method. The model achieves an mAP value of 75.5%, showcasing a notable 7.9% enhancement in the detection AP value for cells falling within the small target category, surpassing other models of a similar nature.

关键词： object detection Cervical cancer Transformer Small targets

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CR-FOD: Foreign object detection based on contrastive analysis of key regions 3

CR-FOD: Foreign object detection based on contrastive analys...

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3rd international conference on Machine Vision, Automatic Identification, and detection, MVAID 2024

作者： Liu, Peigang Lan, Qingsong He, Peng Deng, Ping Liang, Cheng Wang, Yuming The Second Research Institute of CAAC Chengdu610000 China Civil Aviation Electronic Technology Co Ltd Chengdu610000 China

ISBN: (纸本)9781510681880

In the realms of flight safety, agricultural monitoring and medical diagnostics, the detection of foreign objects within images processing technology is of importance. While recent deep learning-based detection methods have enhanced overall performance compared to traditional image techniques, they often encounter challenges in identifying small or novel foreign objects due to data biases. To overcome this limitation, a new approach termed foreign object detection based on contrastive analysis of key regions (CR-FOD) has been introduced. CR-FOD is bifurcated into two phases: Foreign object Promoter (FOP) and Contrastive Region object detection (CROD). During FOP, potential foreign object locations are marked through key point comparison. In the CROD phase, objects are extracted from the inference image and the corresponding background image based on key-point prompt information by FOP. Finally, the similarity of the objects is compared by CROD to determine the presence of a foreign object at the location. In addition, the efficacy of CR-FOD is confirmed through experimental testing, demonstrating its superior performance in detecting a wide array of foreign objects, particularly those that are minute or previously not encountered during the training datasets. © 2024 SPIE.

关键词： image enhancement

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Lane detection via Sparse Instance Activation 3

Lane Detection via Sparse Instance Activation

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3rd international conference on image processing and Media Computing (ICIPMC)

作者： Yang, Jun Gu, Yanhong Hu, Yuxia Li, Yuanyuan Nian, Fudong Hefei Univ Sch Adv Mfg Engn Hefei Peoples R China Anhui Jianzhu Univ Anhui Int Joint Res Ctr Ancient Architecture Inte Hefei Peoples R China AnHui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei Peoples R China Anhui Prov Engn Technol Res Ctr Intelligent Vehic Hefei Peoples R China

ISBN: (纸本)9798350386660;9798350386677

The most advanced lane detection methods typically rely on specific knowledge about lanes (such as straight lines and parametric curves) to detect lane lines. Although specific knowledge can simplify the modeling process, it does not work well in handling lane lines with complex topological structures (such as dense, forked, curved, etc.). Recently, methods based on dynamic convolution have shown promising performance by using features of some key positions of the lane lines (such as starting points) as convolutional kernels and convolving them with the entire feature map to detect lane lines. Although such methods reduce the reliance on specific knowledge, the kernels derived from key positions cannot capture the global structure of the lane lines due to their long and thin structure, leading to inaccurate detection of lane lines with complex topologies. In addition, the kernels derived from key positions are sensitive to occlusions and lane intersections. To overcome these limitations, in this paper, we propose to use multiple sets of sparse instance activation maps as the object representation of lane lines, highlighting the information regions of each foreground object. Then, based on the highlighted regions, instance-level features are aggregated. Furthermore, based on the Hungarian algorithm, the instance activation maps can predict lanes in one-to-one manner, avoiding non-maximum suppression (NMS) in post-processing. We evaluated our method on two lane detection benchmarks, and the results proved its advanced performance. Specifically, our method achieved F1 scores of 78.36 and 96.68 on CULane and Tuisimple, respectively.

关键词： Lane detection Bipartite matching Sparse instance activation

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PhD Forum Abstract: Sensor Fusion for Vehicle-side and Roadside 3D object detection and tracking 23

PhD Forum Abstract: Sensor Fusion for Vehicle-side and Roads...

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23rd ACM/IEEE international conference on Information processing in Sensor Networks, IPSN 2024

作者： Li, Yao University of Science and Technology of China Hefei China

ISBN: (纸本)9798350362015

We focus on the sensor fusion for vehicle-side and roadside 3D object detection and tracking. Although quite a few sensor fusion algorithms have been proposed, some of which are top-ranked on various leaderboards, a systematic study on how to integrate three crucial sensors (LiDAR, camera and millimeter-wave Radar sensors) to develop effective multi-modal 3D object detection and tracking for vehicle-side perception is still missing. Therefore, we first study the three sensors' strengths and weaknesses carefully, then compare several different fusion strategies to maximize their utility. Finally, based on the lessons learnt, we propose a simple yet effective multi-modal 3D object detection and tracking framework (namely EZFusion). Without fancy network modules, our proposed EZFusion makes remarkable improvements over the LiDAR-only baseline, and achieves comparable performance. For intelligent transportation, far-range perception with roadside sensors is vital. The main challenge of far-range perception is performing accurate object detection and tracking under far distances (e.g., > 150m) at a low cost. To cope with such challenges, deploying both millimeter wave Radars and high-definition cameras, and fusing their data has become a common practice. Towards this goal, the first question is to conduct the association on the 2D image plane or the BEV plane. We argue that the former is more suitable because the magnitude of location errors in the perspective projection points is smaller at far distances on the 2D plane, leading to more accurate association. Thus, we first project the Radar points to the 2D plane and then associate them with the camera-based 2D object locations. Subsequently, we map the camera-based object locations to the BEV plane through inverse projection mapping (IPM) with the corresponding depth information from the Radar data. Finally, we engage a BEV tracking module to generate target trajectories. Our system is capable of achieving an average l

关键词： Cameras

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Automatic Conversion of Broadcasted Football Match Recordings to Its 2D Top View 3rd

Automatic Conversion of Broadcasted Football Match Recording...

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3rd international conference on Artificial Intelligence and Knowledge processing (AIKP)

作者： Barbadekar, Ashwini Mahajan, Anurag Patil, Sanmit Patil, Amitesh Vishwakarma Inst Technol Dept Elect & Telecommun Engn Pune Maharashtra India

ISBN: (纸本)9783031686160;9783031686177

This paper presents an automated method to convert a broadcasted football match recording to its 2D Birds eye view. Representing a football match in 2D can be useful for tactical analysis and generating stats from events in the football match. We perform player detection and tracking on the broadcasted recording using YOLOv7 and DeepSort tracker. A database generation process is discussed to generate images of different views and perspectives of the football field along with their perspective transformation matrices. After performing some pre-processing steps on the frames from the broadcasted recording, it is compared with the images in the database to find the closest match. The perspective transformation matrix of the closest match is then used to convert the input image to its 2D top view.

关键词： Perspective Transformation Player detection and tracking image processing Computer Vision Deep Learning

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Deep Learning based vehicle image detection using Yolo V5 with Region-Based Convolutional Neural Network 3

Deep Learning based vehicle image detection using Yolo V5 wi...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Rani, S. Swapna Mudigonda, Aditya Hemanth, S.V. Sundararajan Reddy, G. Vinoda Amirthayogam, G. Engineering College Department of Electronics and Communication Engineering Hyderabad India JNIAS School of Planning and Architecture Telangana Hyderabad500034 India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Telangana Hyderabad501401 India Hindustan Institute of Technology and Science Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350372120

Detecting and classifying vehicles is a modern technology with numerous uses. Administration and regulation of traffic is one of the primary uses. Projects utilizing image processing to prevent traffic accidents heavily rely on vehicle monitoring and detection. Monitoring and recording human movement in surveillance situations require the ability to follow moving objects. Considering its significance, it provides a valuable image processing-based vehicle detection technique - a vehicle tracking and detection system based on images. The recently released high-resolution road vehicle dataset supports deep learning-based vehicle detection and monitoring on the Python platform. It includes over 100 well-defined pictures taken from movies from various locations. This data is produced using a range of image processing methods. The most recent iteration of the YOLO model, the v5 model, was employed for detection. R-CNN is additionally utilized for model detection. The four stages in the Region-Based Convolutional Neural Network (RCNN) process are preprocessing, segmentation, feature selection, and classification. The first step is identifying moving vehicles accurately. Preprocessing the original image is necessary. This enhances the image's quality, valuable information extracted, or unwanted areas cropped out. Segmentation is the second step. The region's borders are established, and the entire image is divided into numerous smaller areas using a threshold. The vehicle image's most pertinent aspects or characteristics (features) that aid in precise detection are found in the third stage, feature selection. A region-based convolutional neural network (RCNN) is employed to determine the categorization. The Yolov5 method can be used to identify and categorize items. The Region-based Convolutional Neural Network (RCNN) approach enhances accuracy and temporal complexity over current segmentation algorithms. © 2024 IEEE.

关键词： Vehicles

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A Certain Investigation on Undersea Water image object detection and Classification Using Artificial Intelligence Algorithms 3rd

A Certain Investigation on Undersea Water Image Object Detec...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Sandhya, Kaipa Arumugam, Jayachandran Presidency University Bangalore India

ISBN: (纸本)9789819708918

The increase in the usage of marine resources in recent years has drawn attention toward the underwater image processing research field;however, underwater images face some of the challenges like severe absorption and scattering of water, low contrast, monotonous color, etc., and make a complicated process. In this paper, we made a comprehensive study related to underwater image processing in recent years. The study includes both machine learning and deep learning approaches used for the detection of underwater objects. This study also presents the current scenarios along with future trends and provides better insights into the research direction in underwater image processing. Recently, artificial intelligence (AI) techniques are widely used for the detection and classification process of undersea water object detection and they demonstrated good outcomes. This study categorizes AI into machine learning and deep learning techniques. This study analyzed 31 papers related to underwater image object detection from various publishers like IEEE, Springer, Elsevier, Taylor and Francis, etc. More survey analysis is based on the study of the deep learning model. Hence, this study presents the advantages and the disadvantages of each model with open research challenges of undersea water object detection and classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Water absorption

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Visual attention estimation and cascade region enhancement detection strategy for intelligent vehicles 3

Visual attention estimation and cascade region enhancement d...

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3rd international conference on Electronic Information Engineering and Data processing, EIEDP 2024

作者： Zhao, Baixuan Zhu, Wangwang Hu, Shuang Hu, Chuan Zhang, Xi School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781510680531

Vision-based object detection in intelligent vehicles presents unique challenges distinct from regular object detection tasks. It requires identifying a critical attention region that significantly influences the vehicle's driving behavior, determined by the ego-vehicle's driving state and the behavior of surrounding traffic participants. Traditional approaches process the entire image uniformly, leading to unnecessary computational overhead. This study introduces a novel approach to precisely and efficiently estimate the attention region, enhancing the detection of crucial objects within it and overall detection performance. Firstly, the method proposes an attention region estimation technique. This technique treats the computation of the attention region as calculating a mass center, considering the ego-vehicle's driving state and nearby traffic participants. Subsequently, the study proposes a cascade region enhancement detection strategy. Utilizing the estimated attention region, this strategy adaptively alternates between lightweight and heavyweight models for the detection tasks, optimizing performance and resource usage. To validate the proposed method's effectiveness, a real-vehicle dataset is compiled, featuring critical objects annotated by drivers. Comparative results demonstrate that the proposed approach significantly boosts efficiency while maintaining high accuracy in detecting critical objects, surpassing the performance of baseline models. © 2024 SPIE.

关键词： object detection

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Enhanced image processing for Automobile Accident detection Using Deep Learning 3

Enhanced Image Processing for Automobile Accident Detection ...

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3rd international conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Usharani, S. Dhanushkumar, J. Manju Bala, P. Balachandar, A. IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Machine Learning Villupuram India IFET College of Engineering An Autonomous Institution Department of Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350391565

Road safety can be creatively increased by utilizing systems for reporting and detecting accidents use the YOLO algorithm. Yolo, which stands for "You Only Look Once,"is a sophisticated object recognition system that can identify and pinpoint objects in live video streams. By identifying accidents and notifying emergency services, the system lowers the reaction times and increases the possibility of saving lives by utilizing the YOLO algorithm. The object detection and alarm systems are the two primary parts of the proposed system. The YOLO method is employed by the object recognition module to look for accidents in live video broadcasts. A module of accident photos was used to teach the system to accurately identify incidents. When an accident is discovered, an alert system is activated. The location of the accident and a brief account of what transpired are communicated to the emergency services by the alert system. This information is communicated to emergency services through a wireless communication network, which expedites response times and increases the likelihood of saving lives. Positive results were obtained by testing the system using the images and an accident module. It was shown that the warning system could react in a couple of seconds and that the YOLO algorithm could identify accidents having a precision of around 94. Highways, busy intersections, and other high-risk areas may have systems in place to increase traffic safety and lower the number of accidents. Developing crash detection and warning systems using deep learning using the YOLO technique is one way to potentially increase road safety. With the use of technology, problems may be precisely identified in real-time video feeds, alerting emergency services and potentially increasing survival rates and reaction times. © 2024 IEEE.

关键词： image enhancement

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