A lightweight network for improving wheat ears detection and counting based on YOLOv5s - Shen, Xiaojun; Zhang, Chu; Liu, Kai; Mao, Wenjie; Zhou, Cheng; Yao, Lili (Frontiers in Plant Science, 2023)
Recognizing wheat ears plays a crucial role in predicting wheat yield. While numerous studies have been conducted on this topic, achieving efficient wheat ear recognition with limited hardware resources remains challenging. This study proposes a lightweight method for detecting and counting wheat ears based on YOLOv5s. It utilizes the ShuffleNetV2 lightweight convolutional neural network to optimize the YOLOv5s model by reducing the number of parameters and simplifying the complexity of the calculation processes. In addition, a lightweight upsampling operator content-aware reassembly of features is introduced in the feature pyramid structure to eliminate the impact of the lightweight process on the model detection performance. This approach aims to improve the spatial resolution of the feature images, enhance the effectiveness of the perceptual field, and reduce information loss. Finally, by introducing the dynamic target detection head, the shape of the detection head and the feature extraction strategy can be dynamically adjusted, and the detection accuracy can be improved when encountering wheat ears with large-scale changes, diverse shapes, or significant orientation variations. This study uses the global wheat head detection dataset and incorporates the local experimental dataset to improve the robustness and generalization of the proposed model. The weight, FLOPs, and mAP of this model are 2.9 MB, 2.5 * 10^9, and 94.8%, respectively. The linear fit-ting determination coefficients R2 for the model test result and actual value of the global wheat head detection dataset and local experimental Site are 0.94 and 0.97, respectively. The improved lightweight model can better meet the requirements of precision wheat ear counting and play an important role in embedded systems, mobile devices, or other hardware systems with limited computing resources.