Research on multi-cluster green persimmon detection method based on improved Faster RCNN - Liu, Yangyang; Ren, Huimin; Zhang, Zhi; Men, Fansheng; Zhang, Pengyang; Wu, Delin; Feng, Ruizhuo (Frontiers in Plant Science, 2023)
To address the problem of accurate recognition and localization of multiple clusters of green persimmons with similar color to the background under natural environment, this study proposes a multi-cluster green persimmons recognition method based on improved Faster RCNN. The feature extractor DetNet is used as the backbone feature extraction network, and the detection accuracy of the algorithm is improved by adding the weighted channel attention module ECA channel attention mechanism to the three effective feature layers in the backbone. By maximizing the pooling of the lower layer features with the added attention mechanism, the high and low dimensions and magnitudes are made the same. The processed feature layers are combined with multi-scale features using a serial layer-hopping connection structure to enhance the robustness of feature information and improve the model’s capacity for generalization. In this study, the K-means clustering algorithm is used to group and anchor the bounding boxes so that they converge to the actual bounding boxes. The average mean accuracy (mAP) of the improved Faster RCNN model reaches 98.4%, which was 11.8% higher than that of traditional Faster RCNN model, and the average detection time of a single image is improved by 0.54s. The algorithm is significantly improved in terms of accuracy and speed, which provides a basis for green fruit growth state monitoring and intelligent yield estimation in real scenarios.