AUTHOR=Wang Xuewei , Liu Jun 

TITLE=Tomato Anomalies Detection in Greenhouse Scenarios Based on YOLO-Dense

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.634103

DOI=10.3389/fpls.2021.634103

ISSN=1664-462X

ABSTRACT=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.