Enhanced Multi-scale Target Detection Method Based on YOLOv5

doi:10.12382/bgxb.2022.1147

Abstract

Abstract:

To address the problem that the initial anchor box is difficult to match the target and its multi-scale detection ability is not strong in complex scenes, an enhanced multi-scale target detection method based on YOLOv5 is proposed. Through the Kmeans++ clustering algorithm, the multi-scale initialization anchors suitable for the current detection scene is obtained, which makes it easier for the network to capture targets with different scales; then, a number of parallel convolution branches with different scales are added to the Bottleneck structure. While retaining the original feature information, the multi-scale feature information is fused to enhance the global perception ability of the model. The EM-YOLOv5s model proposed is tested on VisDrone2019, COCO2017, and PASCAL VOC2012 datasets. The experimental results show that: compared with the YOLOv5s model, the key indicators such as mAP@0.5∶0.95 and mAP@0.5 are improved; on PASCAL VOC2012, mAP @0.5∶0.95 is increased by 5.2%, while the detection time is only increased by 1.9ms, indicating that EM-YOLOv5 model can effectively improve the target detection accuracy in general complex scenes.

Key words: YOLOv5 model, target detection, clustering algorithm, multi-scale convolution, feature fusion

CLC Number:

TP391.41

HUI Kanghua, YANG Wei, LIU Haohan, ZHANG Zhi, ZHENG Jin, BAI Xiao. Enhanced Multi-scale Target Detection Method Based on YOLOv5[J]. Acta Armamentarii, 2023, 44(9): 2600-2610.

Figures/Tables 16

References 34

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锚框算法	数据集	召回率/%	mAP (0.5)/%	mAP 0.5∶0.95/%
Kmeans	VOC2012	78.8	84.1	60.4
Kmeans++	VOC2012	79.8	84.7	60.8
GMM	VOC2012	79.6	84.8	61.0
Agglomerative	VOC2012	76.6	83.8	58.7
Kmeans	COCO	51.7	57.2	37.8
Kmeans++	COCO	52.7	57.4	38.1
GMM	COCO	50.2	54.3	34.6
Agglomerative	COCO	50.8	55.6	35.4
Kmeans	VisDrone	33.5	34.5	18.8
Kmeans++	VisDrone	35	35.4	19.4
GMM	VisDrone	32.6	33.8	16.6
Agglomerative	VisDrone	33.2	34.1	17.2

锚框算法	数据集	召回率/%	mAP (0.5)/%	mAP 0.5∶0.95/%
Kmeans	VOC2012	78.8	84.1	60.4
Kmeans++	VOC2012	79.8	84.7	60.8
GMM	VOC2012	79.6	84.8	61.0
Agglomerative	VOC2012	76.6	83.8	58.7
Kmeans	COCO	51.7	57.2	37.8
Kmeans++	COCO	52.7	57.4	38.1
GMM	COCO	50.2	54.3	34.6
Agglomerative	COCO	50.8	55.6	35.4
Kmeans	VisDrone	33.5	34.5	18.8
Kmeans++	VisDrone	35	35.4	19.4
GMM	VisDrone	32.6	33.8	16.6
Agglomerative	VisDrone	33.2	34.1	17.2

结构	数据集	召回率/%	mAP@ 0.5/%	mAP@ 0.5∶0.95/%	参数量
C3	VOC2012	78.8	84.1	60.4	7.0
C3	VisDrone	33.5	34.5	18.8	7.0
EM-C3	VOC2012	79.9	85.7	65.4	8.4
EM-C3	VisDrone	35.2	35.8	20.4	8.4

结构	数据集	召回率/%	mAP@ 0.5/%	mAP@ 0.5∶0.95/%	参数量
C3	VOC2012	78.8	84.1	60.4	7.0
C3	VisDrone	33.5	34.5	18.8	7.0
EM-C3	VOC2012	79.9	85.7	65.4	8.4
EM-C3	VisDrone	35.2	35.8	20.4	8.4

模型	召回率/%	mAP @0.5/%	mAP@ 0.5∶0.95/%	参数量/M
YOLOv5s	78.8	84.1	60.4	7.0
YOLOv5m	82.0	87.6	67.1	20.9
YOLOv5l	85.1	89.1	69.3	46.2
YOLOv5s-EM_C3	79.9	85.7	65.4	8.4
YOLOv5m-EM_C3	84.2	88.3	69.2	24.2
YOLOv5l-EM_C3	85.5	89.3	70.6	52.1