关键词: 田间场景, 轻量级网络模型, 边缘设备, 实时检测, 适用性

Abstract: In order to achieve fast and accurate obstacle detection in vineyard field on unmanned agricultural machinery, four kinds of lightweight target detection neural networks, EfficientDetD0, YOLOV4TINY, YOLOV3TINY, and YOLOFASTEST, were trained with the independently built vineyard field scene dataset,and the training model eretransplanted to the edge device NVIDIA JETSON TX2. The accuracy of obstacle detection and the applicability of the four models on TX2 were evaluated. The results showed that the mAP of YOLOV3TINY, YOLOV4TINY, EfficientDetD0, and YOLOFASTEST were 0.648, 0.601, 0.598, and 0401, respectively. The experimental results on TX2 showed that the realtime video detection frame rates of the above network models were 34.24 frames, 24.75 frames, 2.34 frames, and 2.97 frames, respectively. Among the four target detection networks, YOLOV3TINY has the highest detection accuracy on the dataset and the fastest realtime detection speed, but it also consumes high hardware resources relatively. When considering the hardware resource consumption, YOLOV4TINY can better balance detection accuracy, running speed, and hardware resource consumption and achieve good results when running multiple tasks.

Key words: field scene, lightweight network structure, edge equipment, realtime detection, applicability