DSR-YOLO: A lightweight and efficient YOLOv8 model for enhanced pedestrian detection
This paper presents DSR-YOLO, a pedestrian detection network that addresses critical challenges, such as scale variations and complex backgrounds. Built on the lightweight YOLOv8n architecture, it incorporates DCNv4 modules to enhance the detection rates and reduce missed detections by effectively l...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-01-01
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| Series: | Cognitive Robotics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667241325000096 |
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| author | Mustapha Oussouaddi Omar Bouazizi Aimad El mourabit Zine el Abidine Alaoui Ismaili Yassine Attaoui Mohamed Chentouf |
| author_facet | Mustapha Oussouaddi Omar Bouazizi Aimad El mourabit Zine el Abidine Alaoui Ismaili Yassine Attaoui Mohamed Chentouf |
| author_sort | Mustapha Oussouaddi |
| collection | DOAJ |
| description | This paper presents DSR-YOLO, a pedestrian detection network that addresses critical challenges, such as scale variations and complex backgrounds. Built on the lightweight YOLOv8n architecture, it incorporates DCNv4 modules to enhance the detection rates and reduce missed detections by effectively learning key pedestrian features. A new head component enables detection across various scales, whereas RFB modules improve accuracy for smaller or occluded objects. Additionally, we enhance the initial C2f layers with a modified block that integrates SimAM and DCNv4, minimizing the background noise and sharpening the focus on the relevant features. A second version of the C2f block using SimAM and standard convolutions ensures robust feature extraction in deeper layers with optimized computational efficiency. The WIoUv3 loss function was utilized to reduce the regression loss associated with bounding boxes, further boosting the performance. Evaluated on the CityPersons dataset, DSR-YOLO outperformed YOLOv8n with a 14.9 % increase in mAP@50 and 6.3 % increase in mAP@50:95, while maintaining competitive FLOPS, parameter counts, and inference speed. |
| format | Article |
| id | doaj-art-37d1ab422b1241b5b25990fb41f3c4aa |
| institution | DOAJ |
| issn | 2667-2413 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Cognitive Robotics |
| spelling | doaj-art-37d1ab422b1241b5b25990fb41f3c4aa2025-08-20T03:18:15ZengKeAi Communications Co. Ltd.Cognitive Robotics2667-24132025-01-01515216510.1016/j.cogr.2025.04.001DSR-YOLO: A lightweight and efficient YOLOv8 model for enhanced pedestrian detectionMustapha Oussouaddi0Omar Bouazizi1Aimad El mourabit2Zine el Abidine Alaoui Ismaili3Yassine Attaoui4Mohamed Chentouf5Corresponding author.; SDE Team, ENSA of Tangier, Abdelmalek Essaâdi University, Tangier, MoroccoSDE Team, ENSA of Tangier, Abdelmalek Essaâdi University, Tangier, MoroccoSDE Team, ENSA of Tangier, Abdelmalek Essaâdi University, Tangier, MoroccoICES Team, Mohammed V University, Rabat, MoroccoSDE Team, ENSA of Tangier, Abdelmalek Essaâdi University, Tangier, Morocco; Siemens EDA/CSD Calypto - Synthesis Solutions, Rabat, MoroccoSiemens EDA/CSD Calypto - Synthesis Solutions, Rabat, MoroccoThis paper presents DSR-YOLO, a pedestrian detection network that addresses critical challenges, such as scale variations and complex backgrounds. Built on the lightweight YOLOv8n architecture, it incorporates DCNv4 modules to enhance the detection rates and reduce missed detections by effectively learning key pedestrian features. A new head component enables detection across various scales, whereas RFB modules improve accuracy for smaller or occluded objects. Additionally, we enhance the initial C2f layers with a modified block that integrates SimAM and DCNv4, minimizing the background noise and sharpening the focus on the relevant features. A second version of the C2f block using SimAM and standard convolutions ensures robust feature extraction in deeper layers with optimized computational efficiency. The WIoUv3 loss function was utilized to reduce the regression loss associated with bounding boxes, further boosting the performance. Evaluated on the CityPersons dataset, DSR-YOLO outperformed YOLOv8n with a 14.9 % increase in mAP@50 and 6.3 % increase in mAP@50:95, while maintaining competitive FLOPS, parameter counts, and inference speed.http://www.sciencedirect.com/science/article/pii/S2667241325000096Pedestrian detectionDCNv4SimAMYOLOv8RFBWIoUv3 |
| spellingShingle | Mustapha Oussouaddi Omar Bouazizi Aimad El mourabit Zine el Abidine Alaoui Ismaili Yassine Attaoui Mohamed Chentouf DSR-YOLO: A lightweight and efficient YOLOv8 model for enhanced pedestrian detection Cognitive Robotics Pedestrian detection DCNv4 SimAM YOLOv8 RFB WIoUv3 |
| title | DSR-YOLO: A lightweight and efficient YOLOv8 model for enhanced pedestrian detection |
| title_full | DSR-YOLO: A lightweight and efficient YOLOv8 model for enhanced pedestrian detection |
| title_fullStr | DSR-YOLO: A lightweight and efficient YOLOv8 model for enhanced pedestrian detection |
| title_full_unstemmed | DSR-YOLO: A lightweight and efficient YOLOv8 model for enhanced pedestrian detection |
| title_short | DSR-YOLO: A lightweight and efficient YOLOv8 model for enhanced pedestrian detection |
| title_sort | dsr yolo a lightweight and efficient yolov8 model for enhanced pedestrian detection |
| topic | Pedestrian detection DCNv4 SimAM YOLOv8 RFB WIoUv3 |
| url | http://www.sciencedirect.com/science/article/pii/S2667241325000096 |
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