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Dynamic detection method for falling ears of maize harvester based on improved YOLO-V4

International Journal of Agricultural and Biological Engineering 2022年第3期15卷 22-32页

作者： Ang Gao Aijun Geng Zhilong Zhang Ji Zhang Xiaolong Hu Ke Li College of Mechanical and Electrical Engineering Shandong Agricultural UniversityTai’an 271018ShandongChina Shandong Provincial Engineering Laboratory of Agricultural Equipment Intelligence Tai’an 271018ShandongChina Shandong Provincial Key Laboratory of Horticultural Machineries and Equipment Tai’an 271018ShandongChina

Traditional maize ear harvesters mainly rely on manual identification of fallen maize ears,which cannot realize real-time detection of ear *** improved You Only Look Once-V4(YOLO-V4)algorithm is combined with the channel pruning algorithm to detect the dropped ears of maize harvesters.K-means clustering algorithm is used to obtain a prior box matching the size of the dropped ears,which improves the Intersection Over Union(IOU).Compare the effect of different activation functions on the accuracy of the YOLO-V4 model,and use the Mish activation function as the activation function of this *** the calculation of the regression positioning loss function,and use the CEIOU loss function to balance the accuracy of each *** improved Adam optimization function and multi-stage learning optimization technology to improve the accuracy of the YOLO-V4 *** channel pruning algorithm is used to compress the model and distillation technology is used in the fine-tuning of the *** final model size was only 10.77%before compression,and the test set mean Average Precision(mAP)was 93.14%.The detection speed was 112 fps,which can meet the need for real-time detection of maize harvester ears in the *** study can provide technical reference for the detection of the ear loss rate of intelligent maize harvesters.

关键词： maize ear detection YOLO-V4 channel pruning algorithm real-time detection

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