Tomato Anomalies Detection in Greenhouse Scenarios Based on YOLO-Dense - Wang, Xuewei; Liu, Jun (Frontiers in Plant Science, 2021)
Detection of tomato anomalies in complex natural environment is an important research direction in the field of plant science. Automated identification of tomato anomalies is still a challenging task because of its small size and complex background. To solve the problem of tomato anomalies detection in complex natural environment, a novel YOLO-Dense was proposed based on a one-stage deep detection YOLO framework. By adding a dense connection module in the network architecture, the network inference speed of the proposed model can be effectively improved. By using K-means algorithm to cluster the anchor box, nine different sizes of anchor boxes with potential objects to be identified were obtained. The multi-scale training strategy was adopted to improve the recognition accuracy of objects at different scales. The experimental results show that the mAP and detection time of a single image of YOLO-Dense network is 96.41% and 20.28 ms, respectively. Compared with SSD, Faster R-CNN and the original YOLOv3 network, YOLO-Dense model achieved the best performance in tomato anomalies detection under complex natural environment.