计算机科学与探索2024，Vol.18Issue(1)：138-150,13.DOI:10.3778/j.issn.1673-9418.2301034
YOLOv4-Tiny的改进轻量级目标检测算法
Improved YOLOv4-Tiny Lightweight Target Detection Algorithm
摘要
Abstract
Object detection is an important branch of deep learning.A large number of edge devices need lightweight object detection algorithms,but the existing lightweight universal object detection algorithms have problems of low detection accuracy and slow detection speed.To solve this problem,an improved YOLOv4-Tiny algorithm based on attention mechanism is proposed.The structure of the original backbone network of YOLOv4-Tiny algorithm is adjusted,the ECA(efficient channel attention)attention mechanism is introduced,the traditional spatial pyramid pooling(SPP)structure is improved to DC-SPP structure by using void convolution,and the CSATT(channel spatial attention)attention mechanism is proposed.The neck network of CSATT-PAN(channel spatial attention path aggregation network)is formed with the feature fusion network PAN,which improves the feature fusion capability of the network.Compared with the original YOLOv4-Tiny algorithm,the proposed YOLOv4-CSATT algorithm is significantly more sensitive to information and accurate in classification when the detection speed is basically the same.The accuracy is increased by 12.3 percentage points on VOC dataset and 6.4 percentage points is increased on COCO dataset.Moreover,the accuracy is 3.3,5.5,6.3,17.4,10.3,0.9 and 0.6 percentage points higher than the Faster R-CNN,SSD,Efficientdet-d1,YOLOv3-Tiny,YOLOv4-MobileNetv1,YOLOv4-MobileNetv2 and PP-YOLO algorithms respectively on VOC dataset,and 2.8,7.1,4.2,18.0,12.2,2.1 and 4.0 percentage points higher in recall rate,respectively,with an FPS of 94.In this paper,the CSATT attention mechanism is proposed to improve the model's ability to capture spatial channel information,and the ECA attention mechanism is combined with the feature fusion pyramid algorithm to improve the model's feature fusion ability and target detection accuracy.关键词
目标检测/YOLOv4-Tiny算法/注意力机制/轻量级神经网络/特征融合Key words
object detection/YOLOv4-Tiny algorithm/attention mechanism/lightweight neural network/feature fusion分类
信息技术与安全科学引用本文复制引用
何湘杰,宋晓宁..YOLOv4-Tiny的改进轻量级目标检测算法[J].计算机科学与探索,2024,18(1):138-150,13.基金项目
国家社会科学基金重大项目(21&ZD166) （21&ZD166）
国家自然科学基金(61876072,61902153) （61876072,61902153）
江苏省自然科学基金(BK20221535).This work was supported by the Major Project of National Social Science Foundation of China(21&ZD166),the National Natural Sci-ence Foundation of China(61876072,61902153),and the Natural Science Foundation of Jiangsu Province(BK20221535). （BK20221535）
