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

Traditional maize ear harvesters mainly rely on manual identification of fallen maize ears, which cannot realize real-time detection of ear falling. The improved You Only Look Once-V4 (YOLO-V4) algorithm was combined with the channel pruning algorithm to detect the dropped ears of maize harvesters. K-means clustering algorithm was 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 model. Improve the calculation of the regression positioning loss function, and use the CEIOU loss function to balance the accuracy of each category. Use improved Adam optimization function and multi-stage learning optimization technology to improve the accuracy of the YOLO-V4 model. The channel pruning algorithm was used to compress the model and distillation technology was used in the fine-tuning of the model. 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 field. This study can provide a technical reference for the detection of the ear loss rate of intelligent maize harvesters.