REAL-TIME WHEAT DETECTION BASED ON LIGHTWEIGHT DEEP LEARNING NETWORK REPYOLO MODEL

Abstract

Real-time detection has become an essential component in intelligent agriculture and industry. In this paper, a real-time wheat spike detection method based on the lightweight deep learning network RepYOLO is proposed. Addressing the small and densely packed phenotype characteristics of wheat spikes, the channel attention mechanism module CBAM from the traditional YOLOv4 algorithm is introduced and multiple convolutional kernels are merged using a structural reparameterization method. Additionally, the ATSS algorithm is incorporated to enhance the accuracy of object detection. These approaches significantly reduce the model size, improve the inference speed, and lower the memory access cost. To validate the effectiveness of the model, it is trained and tested on a large dataset of diverse wheat spike images representing various phenotypes. The experimental results demonstrate that the RepYOLO algorithm achieves an average accuracy of 98.42% with a detection speed of 8.2 FPS. On the Jetson Nano platform, the inference speed reaches 34.20 ms. Consequently, the proposed model effectively reduces the memory access cost of deep learning networks without compromising accuracy and successfully improves the utilization of CPU/MCU limited performance.