基于改进特征提取及融合模块的YOLOv3模型

doi:10.16180/j.cnki.issn1007-7820.2022.07.007

Abstract

Abstract:

There is a certain optimization space for the feature extraction branch and multi-scale detection branch of YOLOv3 model. To solve this problem, this study proposes two structural improvement methods to improve the detection accuracy of the model on the target detection data set. For the three scales (13×13, 26×26, 52×52) of the YOLOv3 model, a priori anchor frames of different lengths and widths are used, and the label frames of the three scales are the same, and the feature fusion method between the design scales is used to improve the accuracy of the model. In view of the problem of convolutional layer spatial view sharing, the original convolutional layer can be replaced with deformable convolution to improve the accuracy of the model. The test result on the industrial tool library proves that the accuracy of the test set of the improved model is increased by 3.6 MAP when compared with the original YOLOv3.

Key words: object detection, deep learning, multiscale fusion, industrial tool detection, residual module, YOLOv3, IOU loss

CLC Number:

TP274

ZHAO Xuan,ZHOU Fan,YU Hancheng. Improved YOLOv3 Model Based on New Feature Extraction and Fusion Module[J].Electronic Science and Technology, 2022, 35(7): 40-45.

Figures/Tables 11

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References 18

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实验环境	实验设定
(Batchsize)批次	4
图像分辨率	416×416
是否采用多尺度训练	是
输入通道数	3
动量	0.9
Decay	0.000 5
学习率	0.001
优化算法	Adam
数据集大小	11 765
数据集划分	0.9/0.1

Epoch	40	50	55	57	59	最佳
MAP1	63.04	73.00	78.00	56.00	76.50	83.40
MAP2	70.00	75.00	74.00	69.00	82.10	86.62

模型	MAP
Base-YOLOv3	83.40
Up-route	86.62
Up-route + DCN	86.98

模型	1	2	3
Class '0' (Cutting Tools)	86.77	98.29	97.52
Class '1'(Fastener Tools)	77.18	83.70	79.12
Class '2' (Adhesive Tools)	88.62	90.97	92.58
Class '3' (Measuring Tools)	87.76	93.12	91.56
Class '4' (Clamp Tools)	84.11	75.34	80.81
Class '5' (Marker)	79.43	76.44	77.22
Class '6' (Polish Tools)	69.52	76.78	82.67
Class '7' (Protection Tools)	94.26	98.18	94.40
平均	83.40	86.62	86.98

改进方式	结果
改进方法1	无法收敛
改进方法2	无法收敛
改进方法3	无法收敛
改进方法4	最佳精度无明显提升

Improved YOLOv3 Model Based on New Feature Extraction and Fusion Module

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模型	运算速度/ms
改进模型	89
YOLOv3	82