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Unmanned aerial vehicles object detection based on image haze removal under sea fog conditions

IET IMAGE PROCESSING 2022年第10期16卷 2709-2721页

作者： Wang Pikun Wu Ling Qi Jiangxin Dai Jiashuai Naval Univ Engn Wuhan 430033 Hubei Peoples R China

Unmanned aerial vehicles (UAVs) have gradually become a major air threat to ships because of small size, good maneuverability, and low cost. vision-based UAV detection offers one of the main ways to identify and protect against UAVs. Unlike land environment, the weather is complicated at sea. The visibility of an object is undermined by such factors as sea fog and sunlight, which makes it difficult to detect UAVs at sea through vision-based object detection. For the purpose of object detection at sea, this paper proposes a UAV object detection method based on image haze removal. In the proposed method, an improved dark channel haze removal (DCHR) algorithm is utilized to remove haze for and restore video images. Additionally, co-ordinate attention (CoordAttention, CA) is introduced to the lightweight algorithms of You Only Look Once (YOLO) for the object detection in restored video images, so as to improve the precision and speed of detection and reduce the miss rate. Some video images are also taken for detection experiments to verify the feasibility and effectiveness of the proposed method.

关键词： fog image restoration sunlight object detection detection experiments sea fog conditions UAVs vision-based UAV detection unmanned aerial vehicles image haze removal vision-based object detection autonomous aerial vehicles good maneuverability restored video images improved dark channel haze removal algorithm air threat UAV object detection method

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Dynamic error-bounded lossy compression to reduce the bandwidth requirement for real-time vision-based pedestrian safety applications

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2022年第1期19卷 117-131页

作者： Rahman, Mizanur Islam, Mhafuzul Holt, Cavender Calhoun, Jon Chowdhury, Mashrur Univ Alabama Dept Civil Construct & Environm Engn Tuscaloosa AL 35487 USA Clemson Univ Glenn Dept Civil Engn Clemson SC USA Clemson Univ Holcombe Dept Elect & Comp Engn Clemson SC USA

As camera quality improves and their deployment moves to areas with limited bandwidth, communication bottlenecks can impair real-time constraints of an intelligent transportation systems application, such as video-based real-time pedestrian detection. Video compression reduces the bandwidth requirement to transmit the video which degrades the video quality. As the quality level of the video decreases, it results in the corresponding decreases in the accuracy of the vision-based pedestrian detection model. Furthermore, environmental conditions, such as rain and night-time darkness impact the ability to leverage compression by making it more difficult to maintain high pedestrian detection accuracy. The objective of this study is to develop a real-time error-bounded lossy compression (EBLC) strategy to dynamically change the video compression level depending on different environmental conditions to maintain a high pedestrian detection accuracy. We conduct a case study to show the efficacy of our dynamic EBLC strategy for real-time vision-based pedestrian detection under adverse environmental conditions. Our strategy dynamically selects the lossy compression error tolerances that maintain a high detection accuracy across a representative set of environmental conditions. Analyses reveal that for adverse environmental conditions, our dynamic EBLC strategy increases pedestrian detection accuracy up to 14% and reduces the communication bandwidth up to 14 x compared to the state-of-the-practice. Moreover, we show our dynamic EBLC strategy is independent of pedestrian detection models and environmental conditions allowing other detection models and environmental conditions to be easily incorporated.

关键词： Error-bounded lossy compression (EBLC) Efficient bandwidth usage Real-time processing vision-based object detection Pedestrian detection

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SafeFac: Video-based smart safety monitoring for preventing industrial work accidents

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 215卷

作者： Ahn, Jungmo Park, JaeYeon Lee, Sung Sik Lee, Kyu-Hyuk Do, Heesung Ko, JeongGil Ajou Univ Dept Artificial Intelligence 206 Worldcup Ro Suwon Gyeonggi Do South Korea Hyundai MOBIS 203 Teheran Ro Seoul South Korea Yonsei Univ Sch Integrated Technol 50 Yonsei Ro Seoul South Korea Syst R&D Co 150 Jinwei 2 Sandan Ro Pyeongtaek Gyeonggi Do South Korea

This work presents SafeFac, an intelligent camera-based system for managing the safety of factory environ-ments. In SafeFac a set of cameras installed on the assembly line are used to capture images of workers that approach the machinery under hazardous situations to alert system managers and halt the line if needed. Given a challenging set of practical application-level requirements such as multi-camera support and low response latency, SafeFac exploits a YOLOv3-based light-weight human object detection. To address the latency-accuracy tradeoff, SafeFac incorporates a set of algorithms as pre-and post-processing modules and a novel adaptive camera scheduling *** evaluation with a video dataset containing more that 113,000 frames from real assembly line activity shows that SafeFac achieves high precision (99.93%) and recall (96.44%), and SafeFac successfully satisfies such challenging requirements as a ready-for-deployment system for safe factory management.

关键词： vision-based object detection Factory safety management Human-presence detection Deep learning

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Synthetic Image Dataset Development for vision-based Construction Equipment detection

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2022年第5期36卷 04022020-04022020页

作者： Lee, Jin Gang Hwang, Jeongbin Chi, Seokho Seo, JoonOh Sun Moon Univ Coll Engn Div Architecture Asan 31460 Chungcheongnam South Korea Seoul Natl Univ Dept Civil & Environm Engn 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ Inst Construct & Environm Engn 1 Gwanak Ro Seoul 08826 South Korea Hong Kong Polytech Univ Dept Bldg & Real Estate Hong Kong Peoples R China

This paper presents a systematic method to create universally applicable synthetic training image datasets for computer vision-based construction object detection. The synthetic images created by inserting a virtual object of interest into a real site image allows us to minimize the time and effort for training image data collection and annotation. In addition, the use of synthetic images has an additional benefit that training images can be easily customized for a target construction site by considering the context of the site (e.g., different background scenes, camera positions, and angles) and the possible variability of target objects to be detected (e.g., different sizes, locations, rotation angles, and postures) on images. An automated approach proposed in this study attempts to systematically create the synthetic images using the Unity game engine in which context- and variability-related parameters can be controlled. The proposed method was validated by training a deep learning-based object detection algorithm [i.e., a faster regions with convolutional neural network (R-CNN) model] with synthetic images and testing it on real images from earthwork construction sites to detect an excavator. The CNN models trained with synthetic images showed an average precision value of more than 90%;in particular, the classifier using synthetic images outperformed the one using real site images. The detection results also demonstrated an improved capability to capture the high irregularity of a construction object on images when using techniques of context customization and variability randomization. The findings from this study demonstrate the feasibility and practicality of the use of synthetic images for vision-based approaches in a construction domain. Ultimately, the proposed approach serves as an alternative way to build comprehensive image datasets for construction entities, contributing to facilitating vision-based studies on construction.

关键词： Construction site monitoring vision-based object detection Synthetic images Deep learning

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Co-Training for Deep object detection: Comparing Single-Modal and Multi-Modal Approaches

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SENSORS 2021年第9期21卷 3185页

作者： Gomez, Jose L. Villalonga, Gabriel Lopez, Antonio M. Univ Autonoma Barcelona UAB Comp Vis Ctr CVC Bellaterra 08193 Spain Univ Autonoma Barcelona UAB Comp Sci Dept Bellaterra 08193 Spain

Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data-labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on par, at least when using an off-the-shelf depth estimation model not specifically trained on the translated images.

关键词： co-training multi-modality vision-based object detection ADAS self-driving

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Co-Training for On-Board Deep object detection

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IEEE ACCESS 2020年 8卷 194441-194456页

作者： Villalonga, Gabriel Pena, Antonio M. Lopez Univ Autonoma Barcelona UAB Comp Vis Ctr CVC Barcelona 08193 Spain Univ Autonoma Barcelona UAB Dept Comp Sci Barcelona 08193 Spain

Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift;in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation.

关键词： object detection Labeling Task analysis Detectors Data models Predictive models Computer vision Co-training domain adaptation vision-based object detection ADAS self-driving

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Teach robots understanding new object types and attributes through natural language instructions 10

Teach robots understanding new object types and attributes t...

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10th International Conference on Sensing Technology (ICST)

作者： Bao, Jiatong Hong, Ze Tang, Hongru Cheng, Yu Jia, Yunyi Xi, Ning Dept Hydraul Energy & Power Engn Yangzhou Jiangsu Peoples R China Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

ISBN: (纸本)9781509007967

Robots often have limited knowledge about the environment and need to continuously acquire new knowledge in order to collaborate with the humans. To address this issue, this paper presents a method which allows the human to teach a robot new object types and attributes through natural language (NL) instructions. A simple yet robust vision algorithm is proposed to segment objects and describe the relations between objects. The segmented objects as well as their relations are regarded as the basic knowledge of the robot. The NL instructions are processed to domain-specific representations for the robot to identify the target objects. The target objects as well as the object type or attribute labels referred in the NL instructions are collected as training samples for the robot to learn. Experimental results demonstrate the effectiveness and advantages of the proposed method.

关键词： human-robot interaction vision-based object detection natural language processing object recognition

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Highway overhead structure detection using video image sequences

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2003年第2期4卷 67-77页

作者： Chen, Y HRL Labs LLC Malibu CA 90265 USA

A radar-based forward collision warning system has a limitation of not being able to distinguish a stopped vehicle from the false alarms caused by highway overhead structures. In this paper, we describe a system that detects the presence of overhead structures based on video images from an in-vehicle camera. The system also estimates the height and the distance of the structures to the host vehicle, which can be used to help reject false alarms in the radar detections caused by overhead structures. Our system uses a unique horizontal edge projection (HEP) as the primary feature for the detection of the overhead structures, which is computationally simple and robust to noise. Image vertical motion compensation and HEP tracking algorithms are developed to establish the HEP tracks of trajectories of the overhead structures in the image sequence. A least-squares algorithm is presented for the estimation of structure height and distance to the host vehicle using the HEP tracks. Experiments using real image sequences are presented.

关键词： automotive collision warning overhead structure detection video image processing vision-based object detection

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Highway overhead structure detection using video image sequences 5

Highway overhead structure detection using video image seque...

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IEEE 5th International Conference on Intelligent Transportation Systems

作者： Chen, Y HRL Labs LLC Malibu CA 90265 USA

ISBN: (纸本)0780373898

关键词： automotive collision warning overhead structure detection video image processing vision-based object detection

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