A systematic review of object detection from images using deep learning

Abstract Artificial intelligence and deep learning-based techniques undoubtedly are the future of Advanced Driver-Assistance Systems (ADAS) technologies. In this article is presented a technique for detecting, recognizing and tracking pedestrians, vehicles and cyclists along a tramway infrastructure in a complex urban environment by Computer Vision, Deep Learning approaches and YOLOv3 algorithm. Experiments have been conducted in the tramway Line 2 “Borgonuovo –Notarbartolo” (Palermo, Italy) in correspondence of the tramway segments crossing a roundabout having an external diameter of 24 m. A survey vehicle equipped with a video camera was used in the study. The results of the research show that the proposed method is able to search and detect the position and the speed of road users near and over the rails in front of the tram in a very precise way as demonstrate by the estimated values of the Accuracy, Loss and Precision obtained during the neural networks training process. The implementation of this advanced detection method in ADAS systems may increase the safety of novel autonomous trams and autonomous rapid trams (ARTs). 

/pdf/smart-tramway-systems-for-smart-cities-a-deep-learning-2xp4fc8u.pdf

Smart Tramway Systems for Smart Cities: A Deep Learning Application in ADAS Systems

In practice, the object detection algorithm is limited by a complex detection environment, hardware costs, computing power, and chip running memory. The performance of the detector will be greatly reduced during operation. Determining how to realize real-time, fast, and high-precision pedestrian recognition in a foggy traffic environment is a very challenging problem. To solve this problem, the dark channel de-fogging algorithm is added to the basis of the YOLOv7 algorithm, which effectively improves the de-fogging efficiency of the dark channel through the methods of down-sampling and up-sampling. In order to further improve the accuracy of the YOLOv7 object detection algorithm, the ECA module and a detection head are added to the network to improve object classification and regression. Moreover, an 864 × 864 network input size is used for model training to improve the accuracy of the object detection algorithm for pedestrian recognition. Then the combined pruning strategy was used to improve the optimized YOLOv7 detection model, and finally, the optimization algorithm YOLO-GW was obtained. Compared with YOLOv7 object detection, YOLO-GW increased Frames Per Second (FPS) by 63.08%, mean Average Precision (mAP) increased by 9.06%, parameters decreased by 97.66%, and volume decreased by 96.36%. Smaller training parameters and model space make it possible for the YOLO-GW target detection algorithm to be deployed on the chip. Through analysis and comparison of experimental data, it is concluded that YOLO-GW is more suitable for pedestrian detection in a fog environment than YOLOv7. 

/pdf/yolo-gw-quickly-and-accurately-detecting-pedestrians-in-a-18ya1j4d.pdf

YOLO-GW: Quickly and Accurately Detecting Pedestrians in a Foggy Traffic Environment

Person detection has attracted great attention in the computer vision area and is an imperative element in human-centric computer vision. Although the predictive performances of person detection networks have been improved dramatically, they are vulnerable to adversarial patch attacks. Changing the pixels in a restricted region can easily fool the person detection network in safety-critical applications such as autonomous driving and security systems. Despite the necessity of countering adversarial patch attacks, very few efforts have been dedicated to defending person detection against adversarial patch attack. In this paper, we propose a novel defense strategy that defends against an adversarial patch attack by optimizing a defensive frame for person detection. The defensive frame alleviates the effect of the adversarial patch while maintaining person detection performance with clean person. The proposed defensive frame in the person detection is generated with a competitive learning algorithm which makes an iterative competition between detection threatening module and detection shielding module in person detection. Comprehensive experimental results demonstrate that the proposed method effectively defends person detection against adversarial patch attacks. 

Defending Person Detection Against Adversarial Patch Attack by Using Universal Defensive Frame

Person detection has attracted great attention in the computer vision area and is an imperative element in human-centric computer vision. Although the predictive performances of person detection networks have been improved dramatically, they are vulnerable to adversarial patch attacks. Changing the pixels in a restricted region can easily fool the person detection network in safety-critical applications such as autonomous driving and security systems. Despite the necessity of countering adversarial patch attacks, very few efforts have been dedicated to defending person detection against adversarial patch attack. In this paper, we propose a novel defense strategy that defends against an adversarial patch attack by optimizing a defensive frame for person detection. The defensive frame alleviates the effect of the adversarial patch while maintaining person detection performance with clean person. The proposed defensive frame in the person detection is generated with a competitive learning algorithm which makes an iterative competition between detection threatening module and detection shielding module in person detection. Comprehensive experimental results demonstrate that the proposed method effectively defends person detection against adversarial patch attacks.

pdf/defending-person-detection-against-adversarial-patch-attack-3vtt320b.pdf

Detection of panoramic vision pedestrian based on deep learning

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Detection of panoramic vision pedestrian based on deep learning