Research on sugarcane seed bad bud recognition method based on improved YOLOv4
In order to realize the real-time detection and elimination of the bad buds by the sugarcane seed cutting mechanism,a rapid recognition method for the bad buds of sugarcane seed based on improved YOLOv4 was proposed.A lightweight Convolutional Block Attention Module(CBAM)was added to the YOLOv4 backbone network to enhance the ability of sugarcane bud feature extraction and reduce the influence of background noise on the accuracy of sugarcane bud recognition.The K-means algorithm was used to re-cluster the data set to generate an anchor frame that was consistent with the characteristics of cane buds,which improved the detection accuracy of cane seed bad buds.The original standard convolution in the path aggregation network is replaced by deep separable convolution,which greatly reduces the parameters and computational load,and improves the overall recognition speed.The training and test results show that compared with YOLOv4,the precision of the improved network model is increased by 3.12%,the mean average precision is increased by 4.15%,the recall is increased by 3.69%,and the recognition time of single image is shortened by 7 ms.The improved algorithm realized rapid and accurate identification of sugarcane seed bad buds and met the need of real-time detection and removal of sugarcane seed bad buds by the seed cutting mechanism.
sugarcane seed bad budimproved the networkseed cutting mechanismattention moduleclustering algorithmdepth separable convolution
NETL
NSTL
万方数据
