An Improved YOLOv5 Algorithm for Vulnerable Road User Detection

Yang, Wei; Tang, Xiaolin; Jiang, Kongming; Fu, Yang; Zhang, Xinling

doi:10.3390/s23187761

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…They added the CBAM attention mechanism to the original model. Yang et al [9] propose an improved YOLOv5 for vulnerable road user detection. They combined Complete Intersection over Union-Loss (CIoU-Loss) and Distance Intersection over Union-Non-Maximum Suppression (DIoUNMS) to improve the model's convergent speed.…”

Section: Related Workmentioning

confidence: 99%

SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection

Niu,

Song,

Zhao

2023

Applied Sciences

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Against the backdrop of ongoing urbanization, issues such as traffic congestion and accidents are assuming heightened prominence, necessitating urgent and practical interventions to enhance the efficiency and safety of transportation systems. A paramount challenge lies in realizing real-time vehicle monitoring, flow management, and traffic safety control within the transportation infrastructure to mitigate congestion, optimize road utilization, and curb traffic accidents. In response to this challenge, the present study leverages advanced computer vision technology for vehicle detection and tracking, employing deep learning algorithms. The resultant recognition outcomes provide the traffic management domain with actionable insights for optimizing traffic flow management and signal light control through real-time data analysis. The study demonstrates the applicability of the SE-Lightweight YOLO algorithm, as presented herein, showcasing a noteworthy 95.7% accuracy in vehicle recognition. As a prospective trajectory, this research stands poised to serve as a pivotal reference for urban traffic management, laying the groundwork for a more efficient, secure, and streamlined transportation system in the future. To solve the existing vehicle detection problems in vehicle type recognition, recognition and detection accuracy need to be improved, alongside resolving the issues of slow detection speed, and others. In this paper, we made innovative changes based on the YOLOv7 framework: we added the SE attention transfer mechanism in the backbone module, and the model achieved better results, with a 1.2% improvement compared with the original YOLOv7. Meanwhile, we replaced the SPPCSPC module with the SPPFCSPC module, which enhanced the trait extraction of the model. After that, we applied the SE-Lightweight YOLO to the field of traffic monitoring. This can assist transportation-related personnel in traffic monitoring and aid in creating big data on transportation. Therefore, this research has a good application prospect.

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Section: Related Workmentioning

confidence: 99%

SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection

Niu,

Song,

Zhao

2023

Applied Sciences

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“…The rapid development of computer vision and deep learning technologies [1][2][3] has brought revolutionary changes to the field of vehicle detection [4][5][6]. Deep learning, in particular, has made significant contributions to vehicle detection, especially in complex environments.…”

Section: Introductionmentioning

confidence: 99%

“…(1) We introduce YOLO-IR-Free, an anchor-free-based algorithm, aimed at fulfilling the real-time detection requirements in infrared images. (2) To address the challenge of capturing vehicle features in complex environments, we incorporate novel attention mechanisms and network modules to enhance the model's capturing capabilities. (3) Through extensive experimental validation, our YOLO-IR-Free algorithm demonstrates outstanding performance in key performance metrics such as accuracy, recall, and F1 score.…”

Section: Introductionmentioning

confidence: 99%

YOLO-IR-Free: An Improved Algorithm for Real-Time Detection of Vehicles in Infrared Images

Zhang,

Huang,

Hei

et al. 2023

Sensors

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In the field of object detection algorithms, the task of infrared vehicle detection holds significant importance. By utilizing infrared sensors, this approach detects the thermal radiation emitted by vehicles, enabling robust vehicle detection even during nighttime or adverse weather conditions, thus enhancing traffic safety and the efficiency of intelligent driving systems. Current techniques for infrared vehicle detection encounter difficulties in handling low contrast, detecting small objects, and ensuring real-time performance. In the domain of lightweight object detection algorithms, certain existing methodologies face challenges in effectively balancing detection speed and accuracy for this specific task. In order to address this quandary, this paper presents an improved algorithm, called YOLO-IR-Free, an anchor-free approach based on improved attention mechanism YOLOv7 algorithm for real-time detection of infrared vehicles, to tackle these issues. We introduce a new attention mechanism and network module to effectively capture subtle textures and low-contrast features in infrared images. The use of an anchor-free detection head instead of an anchor-based detection head is employed to enhance detection speed. Experimental results demonstrate that YOLO-IR-Free outperforms other methods in terms of accuracy, recall rate, and average precision scores, while maintaining good real-time performance.

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