A Real‐Time Object Detector for Autonomous Vehicles Based on YOLOv4

Wang, Rui; Wang, Ziyue; Xu, Zhengwei; Wang, Chi; Li, Qiang; Zhang, Yuxin; Li, Hua

doi:10.1155/2021/9218137

Cited by 27 publications

(14 citation statements)

References 33 publications

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“…Readers should note that the tables use the accuracy parameter as a representation of system performance (the same method was used in similar papers, e.g. [56]), but there are other parameters (such as IoU) that may give different insights into the system's functioning but were not used to report results in this paper.…”

Section: Resultsmentioning

confidence: 99%

System for automatic detection and classification of cars in traffic

Bralić

Musić

2022

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Objective: To develop a system for automatic detection and classification of cars in traffic in the form of a device for autonomic, real-time car detection, license plate recognition, and car color, model, and make identification from video.Methods: Cars were detected using the You Only Look Once (YOLO) v4 detector. The YOLO output was then used for classification in the next step. Colors were classified using the k-Nearest Neighbors (kNN) algorithm, whereas car models and makes were identified with a single-shot detector (SSD). Finally, license plates were detected using the OpenCV library and Tesseract-based optical character recognition. For the sake of simplicity and speed, the subsystems were run on an embedded Raspberry Pi computer.Results: A camera was mounted on the inside of the windshield to monitor cars in front of the camera. The system processed the camera’s video feed and provided information on the color, license plate, make, and model of the observed car. Knowing the license plate number provides access to details about the car owner, roadworthiness, car or license place reports missing, as well as whether the license plate matches the car. Car details were saved to file and displayed on the screen. The system was tested on real-time images and videos. The accuracies of car detection and car model classification (using 8 classes) in images were 88.5% and 78.5%, respectively. The accuracies of color detection and full license plate recognition were 71.5% and 51.5%, respectively. The system operated at 1 frame per second (1 fps).Conclusion: These results show that running standard machine learning algorithms on low-cost hardware may enable the automatic detection and classification of cars in traffic. However, there is significant room for improvement, primarily in license plate recognition. Accordingly, potential improvements in the future development of the system are proposed.

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Section: Resultsmentioning

confidence: 99%

System for automatic detection and classification of cars in traffic

Bralić

Musić

2022

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“…To objectively validate the target identification performance of the proposed method in this research, the suggested method is compared to some advanced methods on the BDD-IW dataset under identical settings. Among these techniques are Faster R-CNN (Girshick, 2015 ), SSD (Liu et al, 2016 ), YOLOv3 (Redmon and Farhadi, 2018 ), YOLOv4 (Bochkovskiy et al, 2020 ), YOLOv5 (Ultralytics), YOLOv6 (Li et al, 2022 ), YOLOX (Ge et al, 2021b ), RT-YOLOv4 (Wang R. et al, 2021 ), TPH-YOLOv5 (Zhu et al, 2021 ), and PPYOLOE (Xu et al, 2022 ). The BDD-IW dataset comparison findings are displayed in Table 3 .…”

Section: Designs For Experimentsmentioning

confidence: 99%

Real-time vehicle target detection in inclement weather conditions based on YOLOv4

Wang

Zhao

et al. 2023

Front. Neurorobot.

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As a crucial component of the autonomous driving task, the vehicle target detection algorithm directly impacts driving safety, particularly in inclement weather situations, where the detection precision and speed are significantly decreased. This paper investigated the You Only Look Once (YOLO) algorithm and proposed an enhanced YOLOv4 for real-time target detection in inclement weather conditions. The algorithm uses the Anchor-free approach to tackle the problem of YOLO preset anchor frame and poor fit. It better adapts to the detected target size, making it suitable for multi-scale target identification. The improved FPN network transmits feature maps to unanchored frames to expand the model's sensory field and maximize the utilization of model feature data. Decoupled head detecting head to increase the precision of target category and location prediction. The experimental dataset BDD-IW was created by extracting specific labeled photos from the BDD100K dataset and fogging some of them to test the proposed method's practical implications in terms of detection precision and speed in Inclement weather conditions. The proposed method is compared to advanced target detection algorithms in this dataset. Experimental results indicated that the proposed method achieved a mean average precision of 60.3%, which is 5.8 percentage points higher than the original YOLOv4; the inference speed of the algorithm is enhanced by 4.5 fps compared to the original, reaching a real-time detection speed of 69.44 fps. The robustness test results indicated that the proposed model has considerably improved the capacity to recognize targets in inclement weather conditions and has achieved high precision in real-time detection.

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“…This section offers a brief survey of existing object recognition models in autonomous systems. In [11], a one-phase object recognition method based on YOLOv4 enhances the recognition performance and supports real-time processes. The neck approach substitutes the SPP with the RFB framework, enhances the PAN framework of the feature fusion method, includes the attention Convolution Block Attention Module (CBAM) and CA architecture to neck and backbone architecture, and lastly, minimizes the complete network width.…”

Section: Related Workmentioning

confidence: 99%

Computational Intelligence with Wild Horse Optimization Based Object Recognition and Classification Model for Autonomous Driving Systems

et al. 2022

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Presently, autonomous systems have gained considerable attention in several fields such as transportation, healthcare, autonomous driving, logistics, etc. It is highly needed to ensure the safe operations of the autonomous system before launching it to the general public. Since the design of a completely autonomous system is a challenging process, perception and decision-making act as vital parts. The effective detection of objects on the road under varying scenarios can considerably enhance the safety of autonomous driving. The recently developed computational intelligence (CI) and deep learning models help to effectively design the object detection algorithms for environment perception depending upon the camera system that exists in the autonomous driving systems. With this motivation, this study designed a novel computational intelligence with a wild horse optimization-based object recognition and classification (CIWHO-ORC) model for autonomous driving systems. The proposed CIWHO-ORC technique intends to effectively identify the presence of multiple static and dynamic objects such as vehicles, pedestrians, signboards, etc. Additionally, the CIWHO-ORC technique involves the design of a krill herd (KH) algorithm with a multi-scale Faster RCNN model for the detection of objects. In addition, a wild horse optimizer (WHO) with an online sequential ridge regression (OSRR) model was applied for the classification of recognized objects. The experimental analysis of the CIWHO-ORC technique is validated using benchmark datasets, and the obtained results demonstrate the promising outcome of the CIWHO-ORC technique in terms of several measures.

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A Real‐Time Object Detector for Autonomous Vehicles Based on YOLOv4

Cited by 27 publications

References 33 publications

System for automatic detection and classification of cars in traffic

System for automatic detection and classification of cars in traffic

Real-time vehicle target detection in inclement weather conditions based on YOLOv4

Computational Intelligence with Wild Horse Optimization Based Object Recognition and Classification Model for Autonomous Driving Systems

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