Research and Implementation of Millet Ear Detection Method Based on Lightweight YOLOv5

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 22; p. 9189
• Main Authors: Qiu, Shujin, Li, Yun, Gao, Jian, Li, Xiaobin, Yuan, Xiangyang, Liu, Zhenyu, Cui, Qingliang, Wu, Cuiqing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2023
• Subjects: Accuracy; Agricultural production; algorithmic optimization; Algorithms; Comparative analysis; Datasets; Deep learning; Detectors; Grain; Jetson Nano; lightweight model; Methods; millet ear; Neural networks; Rice; Wheat; YOLOv5
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23229189

As the millet ears are dense, small in size, and serious occlusion in the complex grain field scene, the target detection model suitable for this environment requires high computing power, and it is difficult to deploy the real-time detection of millet ears on mobile devices. A lightweight real-time detection method for millet ears is based on YOLOv5. First, the YOLOv5s model is improved by replacing the YOLOv5s backbone feature extraction network with the MobilenetV3 lightweight model to reduce model size. Then, using the multi-feature fusion detection structure, the micro-scale detection layer is augmented to reduce high-level feature maps and low-level feature maps. The Merge-NMS technique is used in post-processing for target information loss to reduce the influence of boundary blur on the detection effect and increase the detection accuracy of small and obstructed targets. Finally, the models reconstructed by different improved methods are trained and tested on the self-built millet ear data set. The AP value of the improved model in this study reaches 97.78%, F1-score is 94.20%, and the model size is only 7.56 MB, which is 53.28% of the standard YoloV5s model size, and has a better detection speed. Compared with other classical target detection models, it shows strong robustness and generalization ability. The lightweight model performs better in the detection of pictures and videos in the Jetson Nano. The results show that the improved lightweight YOLOv5 millet detection model in this study can overcome the influence of complex environments, and significantly improve the detection effect of millet under dense distribution and occlusion conditions. The millet detection model is deployed on the Jetson Nano, and the millet detection system is implemented based on the PyQt5 framework. The detection accuracy and detection speed of the millet detection system can meet the actual needs of intelligent agricultural machinery equipment and has a good application prospect.