Authors

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.

Abstract in Chinese

实时检测已成为智能农业和工业的重要组成部分。本文提出了一种基于轻量级深度学习网络RepYOLO的小麦穗实时检测方法。针对小麦穗小而密的表型特征，我们从传统的YOLOv4算法中引入通道注意机制模块CBAM，并使用结构重参数化方法合并多个卷积核。此外，我们还结合了ATSS算法来提高目标检测的准确性。这些方法显著减小了模型尺寸，提高了推理速度，降低了内存访问成本。为了验证该模型的有效性，我们在代表不同表型的不同小麦穗图像的大型数据集上进行了训练和测试。实验结果表明，RepYOLO算法的平均准确率为98.42%，检测速度为8.2fps。在Jetson nano平台上，推理速度达到34.20/ms。因此，本文提出的模型在不影响精度的前提下，有效地降低了深度学习网络的内存访问成本，并成功地提高了CPU/MCU有限性能的利用率。