Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO
The safety inspection system for the underside of vehicles demands both high detection speed and accuracy, necessitating a model with small parameters and high network efficiency. To address these issues and improve the real-time performance of under-vehicle safety inspection, the YOLOv8n object-det...
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MDPI AG
2024-12-01
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| author | Di Zhao Yulin Cheng Sizhe Mao |
| author_facet | Di Zhao Yulin Cheng Sizhe Mao |
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| description | The safety inspection system for the underside of vehicles demands both high detection speed and accuracy, necessitating a model with small parameters and high network efficiency. To address these issues and improve the real-time performance of under-vehicle safety inspection, the YOLOv8n object-detection algorithm was enhanced, resulting in the PSP-YOLO algorithm. Firstly, the improved PSP-YOLO network introduces a P2 small-object detection head, which is particularly effective in detecting tiny objects, such as small dangerous objects under vehicles, thereby significantly enhancing the ability to detect such small targets. Secondly, the Space-to-Depth Conv (SPD-Conv) module was introduced into the backbone network, which improves the detection performance of low-quality samples and small objects while ensuring high precision and accurate localization of small targets in images. Finally, the Partial Convolution (PConv) lightweight module was also added to the feature fusion network, effectively reducing the model’s parameters and computational load, conserving computing resources, and improving detection speed and accuracy. Experimental results demonstrate that the modified model increased mAP@0.5 by 3.9% and mAP@0.5-0.95 by 3.6% on the dataset used, reduced the number of parameters to 2.53 M, and achieved a detection speed of 104.6 f/s. Therefore, the improved YOLOv8n algorithm significantly enhances the detection performance of dangerous objects, meeting the high-precision and real-time requirements for vehicle underside safety inspections. |
| format | Article |
| id | doaj-art-2bc474e67ff5456c8060dccc6f0c2124 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-2bc474e67ff5456c8060dccc6f0c21242025-08-20T02:38:36ZengMDPI AGApplied Sciences2076-34172024-12-0114231125710.3390/app142311257Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLODi Zhao0Yulin Cheng1Sizhe Mao2School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaSchool of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaSchool of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaThe safety inspection system for the underside of vehicles demands both high detection speed and accuracy, necessitating a model with small parameters and high network efficiency. To address these issues and improve the real-time performance of under-vehicle safety inspection, the YOLOv8n object-detection algorithm was enhanced, resulting in the PSP-YOLO algorithm. Firstly, the improved PSP-YOLO network introduces a P2 small-object detection head, which is particularly effective in detecting tiny objects, such as small dangerous objects under vehicles, thereby significantly enhancing the ability to detect such small targets. Secondly, the Space-to-Depth Conv (SPD-Conv) module was introduced into the backbone network, which improves the detection performance of low-quality samples and small objects while ensuring high precision and accurate localization of small targets in images. Finally, the Partial Convolution (PConv) lightweight module was also added to the feature fusion network, effectively reducing the model’s parameters and computational load, conserving computing resources, and improving detection speed and accuracy. Experimental results demonstrate that the modified model increased mAP@0.5 by 3.9% and mAP@0.5-0.95 by 3.6% on the dataset used, reduced the number of parameters to 2.53 M, and achieved a detection speed of 104.6 f/s. Therefore, the improved YOLOv8n algorithm significantly enhances the detection performance of dangerous objects, meeting the high-precision and real-time requirements for vehicle underside safety inspections.https://www.mdpi.com/2076-3417/14/23/11257YOLOv8nsmall-target detectiondangerous-object detectionPConv lightweight modulevehicle safety |
| spellingShingle | Di Zhao Yulin Cheng Sizhe Mao Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO Applied Sciences YOLOv8n small-target detection dangerous-object detection PConv lightweight module vehicle safety |
| title | Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO |
| title_full | Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO |
| title_fullStr | Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO |
| title_full_unstemmed | Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO |
| title_short | Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO |
| title_sort | improved algorithm for vehicle bottom safety detection based on yolov8n psp yolo |
| topic | YOLOv8n small-target detection dangerous-object detection PConv lightweight module vehicle safety |
| url | https://www.mdpi.com/2076-3417/14/23/11257 |
| work_keys_str_mv | AT dizhao improvedalgorithmforvehiclebottomsafetydetectionbasedonyolov8npspyolo AT yulincheng improvedalgorithmforvehiclebottomsafetydetectionbasedonyolov8npspyolo AT sizhemao improvedalgorithmforvehiclebottomsafetydetectionbasedonyolov8npspyolo |