农业大数据学报2024，Vol.6Issue(1)：40-47,8.DOI:10.19788/j.issn.2096-6369.000006
基于改进的YOLOv3农作物目标检测算法
Improved YOLOv3 Crop Target Detection Algorithm
摘要
Abstract
When detecting targets in crop images,the detection accuracy of target detection algorithms can be seriously affected due to factors such as dense crop planting and poor imaging quality.In order to optimize the detection performance of crop object detection in YOLOv3,an improved algorithm based on YOLOv3 is proposed.Firstly,the backbone feature extraction network of YOLOv3 is optimized by utilizing the downsampling feature maps outputted by the original network to detect targets,and residual units are added on the basis of the residual blocks in the original network to detect the position information of small crop objects.Moreover,a Gaussian decay function is introduced to attenuate highly overlapping crop candidate boxes in the image,effectively suppressing redundant boxes and reducing false negative rate.Furthermore,the regression loss function is optimized by using CIOU Loss,making the final object localization more accurate during the object detection process.To evaluate the improved YOLOv3 algorithm,a comparative experiment is conducted on a real-world dataset of maize crop images,comparing it with the original YOLOv3 algorithm and the Faster R-CNN algorithm.The results demonstrate that the improved YOLOv3 algorithm can effectively detect small crop targets,exhibiting significantly improved mean average precision and detection speed.关键词
目标检测/YOLOv3算法/特征提取网络/损失函数Key words
target detection/YOLOv3 algorithm/feature extraction network/loss function引用本文复制引用
郭蓓,王贝贝,张志红,吴苏,李鹏,胡莉婷..基于改进的YOLOv3农作物目标检测算法[J].农业大数据学报,2024,6(1):40-47,8.基金项目
中国气象局·河南省农业气象保障与应用技术重点开放实验室研究基金(AMF202203)资助 （AMF202203）
