Rapid Target Detection of Fruit Trees Using UAV Imaging and Improved Light YOLOv4 Algorithm

作者：Yuchao Zhu, Jun Zhou, Yinhui Yang, Lijuan Liu, Fei Liu, Wenwen Kong · 发表于：Remote Sensing · 年份：2022 · DOI：10.3390/rs14174324 · 被引用次数：36 · 研究领域：Smart Agriculture and AI、Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture

The detection and counting of fruit tree canopies are important for orchard management, yield estimation, and phenotypic analysis. Previous research has shown that most fruit tree canopy detection methods are based on the use of traditional computer vision algorithms or machine learning methods to extract shallow features such as color and contour, with good results. However, due to the lack of robustness of these features, most methods are hardly adequate for the recognition and counting of fruit tree canopies in natural scenes. Other studies have shown that deep learning methods can be used to perform canopy detection. However, the adhesion and occlusion of fruit tree canopies, as well as background noise, limit the accuracy of detection. Therefore, to improve the accuracy of fruit tree canopy recognition and counting in real-world scenarios, an improved YOLOv4 (you only look once v4) is proposed, using a dataset produced from fruit tree canopy UAV imagery, combined with the Mobilenetv3 network, which can lighten the model and increase the detection speed, combined with the CBAM (convolutional block attention module), which can increase the feature extraction capability of the network, and combined with ASFF (adaptively spatial feature fusion), which enhances the multi-scale feature fusion capability of the network. In addition, the K-means algorithm and linear scale scaling are used to optimize the generation of pre-selected boxes, and the learning strategy of cosine annea...