Fruit Target Detection Method based on Improved YOLOv7
LIU Qi1，LI Kuidong1 ，CHANG
Guangliang2，WANG Ying
1.School of Information and Control Engineering，Jilin Institute of Chemical Technology，Jilin City 132022，China2. Kesshida (Changchun) Automobile Electrical Appliance Co., LTD., Changchun130031, China
Abstract: At present, fruits are widely planted, but most of the
fruit classification is done manually, which consumes a lot of labor costs, and
a few machine recognition also has problems such as slow speed and low
accuracy.To solve the problem of
low efficiency of target detection and recognition, a fruit target detection
algorithm based on improved YOLO v7 algorithm is proposed. By introducing ECA
attention mechanism, the relevance of channel dimensions is enhanced, and the
expression ability and learning effect of the model are improved; PConv is used
instead of partial convolution structure, and redundant computing and memory
access are reduced to extract spatial features more effectively; The loss
function uses MPDIoU, which enhances the gradient differentiability for
traditional IoU, facilitating the training and optimization of image
segmentation tasks. The improved YOLOv7 algorithm can accurately recognize
fruits by improving the precision and average accuracy by 4% and 3%
respectively.
刘 麒, 李奎东, 常广良, 王 影. 基于改进YOLOv7的水果目标检测方法[J]. 吉林化工学院学报, 2024, 41(7): 12-17.
LIU Qi , LI Kuidong , CHANG Guangliang, WANG Ying. Fruit Target Detection Method based on Improved YOLOv7. Journal of Jilin Institute of Chemical Technology, 2024, 41(7): 12-17.
