Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism

The information of tomato young fruits acquisition has an important impact on monitoring fruit growth, early control of pests and diseases and yield estimation. It is of great significance for timely removing young fruits with abnormal growth status, improving the fruits quality, and maintaining high and stable yields. Tomato young fruits are similar in color to the stems and leaves, and there are interference factors, such as fruits overlap, stems and leaves occlusion, and light influence. In order to improve the detection accuracy and efficiency of tomato young fruits, this paper proposes a method for detecting tomato young fruits with near color background based on improved Faster R-CNN with an attention mechanism. First, ResNet50 is used as the feature extraction backbone, and the feature map extracted is optimized through Convolutional Block Attention Module (CBAM). Then, Feature Pyramid Network (FPN) is used to integrate high-level semantic features into low-level detailed features to enhance the model sensitivity of scale. Finally, Soft Non-Maximum Suppression (Soft-NMS) is used to reduce the missed detection rate of overlapping fruits. The results show that the mean Average Precision (mAP) of the proposed method reaches 98.46%, and the average detection time per image is only 0.084 s, which can achieve the real-time and accurate detection of tomato young fruits. The research shows that the method in this paper can efficiently identify tomato young fruits, and provides a better solution for the detection of fruits with near color background.