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A Novel Multi-Sensor Fusion Based object detection and recognition Algorithm for Intelligent Assisted Driving

IEEE ACCESS 2021年 9卷 81564-81574页

作者： Liu, Tianbi Du, Shanshan Liang, Chenchen Zhang, Bo Feng, Rui Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Fudan Univ Sch Informat Sci & Technol Shanghai 200433 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 200082 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China

The object detection and recognition algorithm based on the fusion of millimeter-wave radar and high-definition video data can improve the safety of intelligent-driving vehicles effectively. However, due to the different data modalities of millimeter-wave radar and video, how to fuse the two effectively is the key point. The difficulty lies in the data fusion methods such as insufficient adaptability of image distortion in data alignment and coordinate transformation and also the mismatching of information levels of the data to be fused. To solve the problem of data fusion of millimeter wave radar and video, this paper proposes a decision-level fusion method of millimeter-wave radar and high-definition video data based on angular alignment. Specifically, through the joint calibration and approximate interpolation, projected to polar coordinate system, the radar and the camera are angularly aligned in the horizontal direction. Then objects are detected by a deep neural network model from video data, and combined with those detected by radar to make the joint decision. Finally, object detection and recognition task based on the fusion of the two kinds of data is completed. Theoretical analysis and experimental results indicate that the accuracy of the algorithm based on the two data fusion is superior to that of the single detection and recognition algorithm on the basis of millimeter-wave radar or video data.

关键词： Radar Radar imaging Radar detection object detection Millimeter wave radar Cameras Data integration Intelligent assisted driving object detection and recognition multi-sensor fusion millimeter-wave radar high-definition video

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CNN with spatio-temporal information for fast suspicious object detection and recognition in THz security images

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SIGNAL PROCESSING 2019年 160卷 202-214页

作者： Yang, Xi Wu, Tan Zhang, Lei Yang, Dong Wang, Nannan Song, Bin Gao, Xinbo Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Guangzhou 510275 Guangdong Peoples R China Xran Inst Space Radio Technol Xian 710100 Shaanxi Peoples R China Xidian Univ Sch Elect Engn State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China

Owing to low radiation required for human body, complete detection of hazardous materials, noncontact privacy protection, and fast pass capability, terahertz (THz) security screening cameras (TSSCs) are widely deployed in public places. However, the existing TSSCs only report about the rough locations of suspicious objects, while directing the complicated detailed recognition work to security inspectors, thus leading to low recognition efficiency. In this paper, we aim to introduce the artificial intelligence technique, called convolutional neural network (CNN), with the spatio-temporal information of THz security image sequences, to achieve automatic object detection and recognition. Our method is composed of rough detection and detailed recognition. In rough detection, benefiting from the inherent alignment of human parts, the sparse and low-rank decomposition (SLD) is used to excavate the spatio-temporal context information. Specifically, the low-rank part representing the static background is regarded as the human body, while the sparse part representing the displaced target is regarded as the suspicious object. Then, by considering the shape knowledge and performing morphological processing, noise interference was reduced and the rough locations of suspicious objects were determined. In detailed recognition, supervised training was first conducted based on Faster R-CNN model with large-scale object labels. The trained Faster R-CNN could extract high-level semantic features of each anchor, and thus predict the class attribute of each object. Notably, with the results of rough detection, only anchors in the candidate domain are computed. Compared with the conventional full domain computation, this narrow-band approach not only reduces the computational complexity but also decreases the false positives caused by anchors in the background. Extensive experiments were conducted on THz security images, and the Tufts prove that our method achieves high performance with

关键词： Convolutional neural network THz security images object detection and recognition Sparse and low-rank decomposition

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All-Day object detection and recognition for Blind Zones of Vehicles Using Deep Learning

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INTERNATIONAL JOURNAL OF PATTERN recognition AND ARTIFICIAL INTELLIGENCE 2024年第1期38卷 2350035-2350035页

作者： Chia, Tsorng-Lin Liu, Pei-June Huang, Ping-Sheng Ming Chuan Univ Dept Comp & Commun Engn Taoyuan 333 Taiwan Super Micro Comp Inc Taoyua 333 Taiwan Ming Chuan Univ Dept Elect Engn Taoyuan 333 Taiwan

The neglect of perception ability to the surrounding traffic conditions has always been the major cause of traffic accidents and the inattention to blind spots is the most important factor during driving. Existing solutions are facing the problems of using expensive equipment, wrong classification of the target object type, not suitable for nighttime, and incorrectly determining if the target object is in the blind zones. This paper aims to improve driving perception ability by developing an all-day object detection and recognition system with more accurate performance for blind zones. The proposed method uses a general-purpose camera as a single input and a two-stage deep network architecture for object detection and recognition. The proposed system is based on a two-stage cascaded network structure. At first, the style conversion process is performed to convert the daytime and nighttime images with different brightness into consistent brightness. Then the objects in the visual blind zones are detected and identified. Therefore, the accuracy of object detection can be significantly improved. Due to the diversity and complexity of Taiwan's road conditions, the public databases cannot effectively fulfill local application model training needs. Therefore, we have built training data set from available night images. Experimental results show that the proposed method has demonstrated promising performance in all-day object detection and recognition for blind zones.

关键词： Blind zones deep learning depth image estimation image style transform object detection and recognition

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Multi-RPN Fusion-based Sparse PCA-CNN Approach to object detection and recognition for Robot-aided Visual System 7

Multi-RPN Fusion-based Sparse PCA-CNN Approach to Object Det...

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7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)

作者： Xia, Chong-Kun Zhang, Yun-Zhou Zhang, Peng-Fei Qin, Cao Zheng, Rui Liu, Shuang-Wei Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9781538604908

object detection and recognition plays an important role in robot-aided visual system. However, when the object's scale varied largely, current object detection algorithms based on convolutional neural network (CNN or ConvNet) (i.e. Fast R-CNN or Faster R-CNN) always cause a high misdetection rate and a low computation speed. In addition, these methods also have the inherent problem: over-fitting. Therefore, we proposed a new multi-RPN fusion-based sparse PCA-CNN detection algorithm to solve the above problems. Firstly, a multi-RPN (Region proposal network) fusion method is adopted to generate candidate windows. Then the new sparse PCA-CNN algorithm is proposed to detect and recognize objects from images or video sequence. Finally, an experiment is adopted to verify the proposed algorithm. And, compared with the existing methods, the results indicate that the proposed method has a better performance.

关键词： object detection and recognition multi-RPN fusion convolutional neural network robot-aided visual system

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Astute Support System for Visually Impaired and Blind with Highest Intersection over Union for object detection and recognition with Voice Feedback

Astute Support System for Visually Impaired and Blind with H...

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18th IEEE Long Island Systems, Applications and Technology Conference

作者： Kariri, Abdulrahman Elleithy, Khaled Univ Bridgeport Comp Sci & Engn Bridgeport CT 06604 USA

ISBN: (纸本)9798331506674;9798331506667

The Visually Impaired and Blind (VIB) face many issues in their daily life on how to detect and recognize their things belongings or around them identifying surroundings without any aid. The VIB frequently relies on outside assistance. Many technologies have been developed to help and guide those who are sight impaired. Since computer vision-based solutions are accessible and reasonably priced, they are becoming increasingly popular among the many technologies used to help the VIB. In this paper, a VIB system is proposed for assistance, and compare the values of Intersection of Union (IoU) with other calculations such as Generalized IoU (GIoU), Distance IoU (DIoU), and Complete IoU (CIoU). Moreover, the average of IoU achieved a good ratio compared to real-time object detection models. The suggested system aims to provide visual assistance for VIB, providing users with voice feedback to help them deal with their lives easily. The system functionality deals with item identification. This will make it easier for blind individuals to go about their daily lives and detect and recognize their environment.

关键词： object detection and recognition Intersection over Union Open CV Python YOLO Voice Feedback and Video capturing

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A New Deep Learning Application Based on Movidius NCS for Embedded object detection and recognition 2

A New Deep Learning Application Based on Movidius NCS for Em...

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2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Othman, Nashwan Adnan Aydin, Ilhan Firat Univ Dept Comp Engn Elazig Turkey

ISBN: (纸本)9781538641842

Nowadays, real-time detection and recognition of objects is a vital task in image processing and computer vision. This study presents an embedded powerful technique for real-time object detection and recognition that runs at high frames per second (FPS) on an embedded platform with movidius neural compute stick (NCS). This can be done by applying a deep learning for computer vision. We recommended an object detection and recognition for real-time video by using deep learning technique and OpenCV libraries. It includes the single shot detector (SSD) algorithm with a MobileNet architecture that are trained with caffe framework. In this paper, Raspberry Pi 3 was utilized to implement this system. So, it helps to monitor and captures the frames and detect and recognize the objects. Also, we used movidius neural compute stick that can be utilized with the Raspberry Pi 3 to achieve high FPS. The proposed method applies a few enhancements such as default boxes, multi scale features and depthwise separable convolution. These enhancements permit the proposed system to get a high accuracy in detection and recognition of objects. Engineering

关键词： object detection and recognition Movidius Deep Learning Single Shot Detector MobileNet Raspberry Pi

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EM-SAM: Eye-Movement-Guided Segment Anything Model for object detection and recognition in Complex Scenes 6

EM-SAM: Eye-Movement-Guided Segment Anything Model for Objec...

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6th World Robot Conference (WRC) / Symposium on Advanced Robotics and Automation (SARA)

作者： Li, Jinqi Yu, Yang Zhou, Junfan Wang, Chinan Ling-li Zeng Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

ISBN: (纸本)9798331506100

Over the past few decades, object detection and recognition systems have made great strides relying on convolutional neural networks (CNNs). However, these methods perform poorly in small object detection and complicated natural scenes. There are still plenty of opportunities for improvement in enhancing small object features extraction and eliminating the effects of complex scenes. Compared with computer, human can automatically ignore redundant information in complex scenes, focusing attention on suspected objects. To this end, we proposed an Eye-Movement-Guided Segment Anything Model for object detection and recognition in Complex Scenes Specifically, the framework includes eye movement state classification for acquiring gaze points, object detection based on gaze point segmentation to remove the influence of complex scenes, and target recognition for lowering the confidence threshold. In the object detection using Segment Anything Model (SAM), the object detection success rate was 97.6% +/- 3.19% when the false detection rate was 9.62% +/- 6.45%. Compared to the baseline, the Recall under the object recognition increased from 85.71% to 91.83%, and the MAP increased by 5.33%. Meanwhile, The average time for selection of the framework's targets was 1.35s, improving the user's ability to interact with the environment.

关键词： object detection and recognition eye movement SAM complex scenes

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Implementation of object detection and recognition Algorithms on a Robotic Arm Platform Using Raspberry Pi

Implementation of Object Detection and Recognition Algorithm...

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International Conference on Artificial Intelligence and Data Processing (IDAP)

作者： Kaymak, Cagri Ucar, Aysegul Firat Univ Mechatron Engn Dept Elazig Turkey

ISBN: (纸本)9781538668788

In this paper, it is aimed to implement object detection and recognition algorithms for a robotic arm platform. With these algorithms, the objects that are desired to be grasped by the gripper of the robotic arm are recognized and located. In the experimental setup that established, OWI-535 robotic arm with 4 DOF and a gripper, which is similar to the robotic arms used in the industry, is preferred. Local feature-based algorithms such as SIFT, SURF, FAST, and ORB are used on the images which are captured via the camera to detect and recognize the target object to be grasped by the gripper of the robotic arm. These algorithms are implemented in the software for object recognition and localization, which is written in C++ programming language using OpenCV library and the software runs on the Raspberry Pi embedded Linux platform. In the experimental studies, the performance of the features which are extracted with the SIFT, SURF, FAST, and ORB algorithms are compared. This study, which is first implemented with OWI-535 robotic arm, shows that the local feature-based algorithms are suitable for education and industrial applications.

关键词： object detection and recognition Local feature-based algorithms OWI-535 robotic arm Raspberry Pi OpenCV

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Moving object detection and recognition by using joint transform correlator

Moving object detection and recognition by using joint trans...

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Conference on Optical Information Processing Technology

作者： Wang, WS Zhang, Y Song, HY Guo, YX Changchun Inst Opt & Fine Mech Changchun Peoples R China

ISBN: (纸本)0819447188

Optical image recognition is an important branch of optical information processing. It has extensive practical meaning and applied prospects. In recent years, joint transform correlator has taken place of matched filter, and pure optical experiments have developed into optoelectronic hybrid experiments. Optoelectronic Joint transform correlator has not only the optical advantages of parallel processing, large capacity and high speed, but also the advantages of computers, such as flexibility, programmability and accuracy. In this paper, electrically addressed liquid crystal displays (EALCD) are used as space light modulator (SLM), by the use of EALCD and CCD matrix camera which is controlled by microcomputer, and the optical Fourier transform (OFT) system, the moving object is detected and recognized. Because of using CCD matrix camera to detect the power spectrum of OFT, many kinds of digital processing algorithms can be adopted to improve signal noise ration (SNR), and to detect and recognize accurately moving objects.

关键词： joint transform correlator electrically addressed liquid crystals display CCD matrix camera object detection and recognition

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Boosting the Accuracy of AdaBoost for object detection and recognition 14

Boosting the Accuracy of AdaBoost for Object Detection and R...

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14th International Conference on Frontiers of Information Technology (FIT)

作者： Mahrnood, Zahid Nazeer, Muhammad Arif, Muhammad Shahzad, Imran Khan, Fahad Ali, Mazhar Khan, Uzair Khan, Samee U. COMSATS Inst Informat Technol Dept Elect Engn Abbottabad Pakistan COMSATS Inst Informat Technol Dept Math Wah Cantt Pakistan COMSATS Inst Informat Technol Dept Comp Sci Abbottabad Pakistan North Dakota State Univ Dept Elect & Comp Engn Fargo ND USA

ISBN: (纸本)9781509053001

Recently, large number of object detection and recognition algorithms are playing a key role in several applications, such as security and surveillance. Although, these algorithms perform exceptionally well under normal lighting conditions, however their detection and recognition accuracy abruptly degrades under non-uniform illuminations, such as strong sunlight and bad lighting conditions. In this paper, we apply our own developed Multi-Scale Retinex (MSR) algorithm as a pre-processing module to boost the accuracy of the AdaBoost algorithm, which is considered to be state-of-the-art algorithm for robust object detection and recognition. Simulation results show that the MSR can be reliably and effectively used under non-uniform illuminations to boost the accuracy of the AdaBoost.

关键词： AdaBoost Multi-Scale Retinex object detection and recognition

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