基于梯度差自适应学习率优化的改进YOLOX目标检测算法

doi:10.7527/S1000-6893.2022.27951

Abstract

Abstract:

Object detection has always been one of the most challenging problems in the field of computer vision， and is widely used in the tasks such as face recognition， autonomous driving and traffic detection. To further improve the performance of current mainstream object detection algorithms， this paper proposes an improved object detection algorithm based on YOLOX， and carries out experiments on the standard PASCAL VOC 07+12 and RSOD datasets. The YOLOX object detection algorithm is improved mainly through data enhancement， improving network structure and loss function. At the same time， an adaptive learning rate optimization algorithm based on gradient difference is proposed to train the improved YOLOX algorithm， which is also suitable for optimization of other neural networks. Experiments are carried out on PASCAL VOC 07+12 standard data sets. Results show that the AP of the improved YOLOX-S algorithm is increased from 61.63% to 66.35% compared with that of the original YOLOX-S algorithm. The improvement effect is obvious. Experiments are also carried out on the RSOD standard data set. The results show that the AP of the improved YOLOX-S algorithm is increased from 69.4% to 73.2% on the RSOD data set， compared with those of other mainstream YOLO series algorithms. The improvement effect is also significant. Experiments show effective improvement of YOLOX’s object detection.

Key words: object detection, YOLOX, neural network optimization, PASCAL VOC, RSOD

CLC Number:

Yucun SONG, Quanbo GE, Junlong ZHU, Zhenyu LU. Improved YOLOX object detection algorithm based on gradient difference adaptive learning rate optimization[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(14): 327951-327951.

Figures/Tables 15

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References 38

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算法	参数量/M	FLOPs/G
改进的YOLOX-S	9.8	31.4
YOLOX-S^［13］	8.9	26.7

算法名称	AP/%	AP提升/%
YOLOX-S^［13］	61.63
+FocalLoss函数	62.65	1.02
+数据增强	62.76	0.11
+SoftTanh激活函数	63.57	0.81
+改进解耦头	64.79	1.22
+本文优化算法	66.35	1.56

算法名称	AP/%	AP₅₀/%	AP₇₅/%	AP_S/%	AP_M/%	AP_L/%
YOLOv3^［10］	70.8	95.1	78.3	47.8	73.2	74.6
YOLOv5s^［12］	67.9	94.3	76.1	46.7	70.4	72.3
YOLOX-S^［13］	69.4	95.4	78.9	49.7	73.2	73.5
YOLOv6s^［34］	70.5	95.9	78.5	44.7	71.7	75.0
改进后YOLOX-S	73.2	97.5	82.3	48.9	73.8	76.9

算法	AP/%	AP₅₀/%	AP₇₅/%	AP_S/%	AP_M/%	AP_L/%
SGDM^［16］	70.7	96.5	79.2	48.3	73.6	75.1
Adam^［22］	60.2	85.4	67.8	49.1	73.3	64.5
AdaBelief^［25］	69.2	95.0	76.9	48.6	71.5	73.5
本文优化算法	72.1	96.9	82.0	48.6	73.5	76.3

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Improved YOLOX object detection algorithm based on gradient difference adaptive learning rate optimization

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