BSD-YOLO: a small target vehicle detection method based on dynamic sparse attention and adaptive detection head

doi:10.11996/JG.j.2095-302X.2026010099

Abstract

Abstract:

In intelligent traffic monitoring systems, small target vehicle detection in complex scenes faces challenges such as low feature resolution, severe occlusion interference, computational redundancy, and insufficient bounding-box regression accuracy. To balance detection accuracy with deployment efficiency on edge devices, an improved YOLOv8 framework based on dynamic sparse attention and a lightweight dual-branch structure was proposed. The method first introduced a bidirectional routing sparse attention mechanism (ReBiAttention) that enhanced the retention of shallow features for small targets by dynamically filtering key features through a two-level routing strategy. Subsequently, GSConv and VoV-GSCSP modules were integrated to reduce computational cost while dynamically adjusting multi-scale feature weights. An improved DynamicHead was applied for multi-task adaptive optimization, and a modified ShapeIoU loss function with shape- and scale-aware weighting was employed to improve localization accuracy. Experiments on the UA-DETRAC dataset showed that, relative to baseline YOLOv8n, Precision, Recall, and mAP@0.5 increased by 8.739%, 1.685%, and 7.225%, respectively, while the parameter count decreased by 4.3%. This method provided an efficient solution for accurate detection of small-target vehicles in complex traffic scenarios.

Key words: YOLOv8, sparse attention, lightweight, deep learning, small target detection

CLC Number:

YANG Biao, WANG Xue, GUAN Zheng, LONG Ping. BSD-YOLO: a small target vehicle detection method based on dynamic sparse attention and adaptive detection head[J]. Journal of Graphics, 2026, 47(1): 99-110.

Figures/Tables 13

References 31

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Top-k	Precision	Recall	mAP@ 0.50	mAP@ 0.50:0.95	GFLOPs
1	0.563 24	0.619 81	0.599 37	0.416 07	11.1
2	0.623 38	0.593 59	0.602 03	0.445 74	11.2
3	0.669 23	0.571 61	0.623 59	0.448 92	11.3
4	0.673 39	0.613 42	0.642 58	0.444 12	11.3
5	0.654 76	0.624 12	0.631 84	0.470 34	11.4
6	0.632 35	0.625 91	0.636 18	0.457 68	11.5
7	0.615 29	0.628 39	0.633 24	0.457 65	11.5
8	0.615 95	0.656 82	0.648 06	0.468 26	11.6

Top-k	Precision	Recall	mAP@ 0.50	mAP@ 0.50:0.95	GFLOPs
1	0.563 24	0.619 81	0.599 37	0.416 07	11.1
2	0.623 38	0.593 59	0.602 03	0.445 74	11.2
3	0.669 23	0.571 61	0.623 59	0.448 92	11.3
4	0.673 39	0.613 42	0.642 58	0.444 12	11.3
5	0.654 76	0.624 12	0.631 84	0.470 34	11.4
6	0.632 35	0.625 91	0.636 18	0.457 68	11.5
7	0.615 29	0.628 39	0.633 24	0.457 65	11.5
8	0.615 95	0.656 82	0.648 06	0.468 26	11.6

编号	参数	设置
1	epochs	300
2	Batch	8
3	imgsz	640
4	workers	4
5	optimizer	SGD
6	close_mosaic	0
7	patience	50
8	warmup_epochs	3.0
9	warmup_momentum	0.8
10	lr0	0.01
11	lrf	0.01
12	mosaic	1.0
13	weight_decay	0.000 5

编号	参数	设置
1	epochs	300
2	Batch	8
3	imgsz	640
4	workers	4
5	optimizer	SGD
6	close_mosaic	0
7	patience	50
8	warmup_epochs	3.0
9	warmup_momentum	0.8
10	lr0	0.01
11	lrf	0.01
12	mosaic	1.0
13	weight_decay	0.000 5

实验方法	Precision	Recall	mAP@0.50	mAP@0.50:0.95	GFLOPs	Params/M
YOLOv8n	0.60881	0.598 17	0.589 04	0.431 27	8.1	3.00
+C2f-ReBiAttention	0.636 09	0.576 85	0.592 03	0.399 98	8.1	2.95
+C3-ReBiAttention	0.639 66	0.486 80	0.571 48	0.417 49	8.0	2.93
+CPN-ReBiAttention	0.646 37	0.563 50	0.598 16	0.424 37	8.0	2.93
+CSC-ReBiAttention	0.642 02	0.566 64	0.584 40	0.409 42	8.0	2.94
+ReBiAttention	0.615 95	0.656 82	0.648 06	0.468 26	11.6	3.45
Ours	0.696 20	0.615 02	0.661 59	0.468 31	7.9	2.87