An improved Yolov3 based on dual path network for cherry tomatoes detection

With the development of deep learning theory, the application of Yolov3 in fruit detection has been widely studied. Aiming at the problem that Yolov3 loses information during network transmission and the semantic feature extraction of small targets is not rich, this article proposed an improved Yolov3 cherry tomato detection algorithm. Firstly, the proposed algorithm uses dual path network as a feature extraction network to extract richer small target semantic features. Second, four feature layers with different scales are established for multiscale prediction. Finally, the improved K-means++ clustering algorithm is used to calculate the scale of anchor boxes. Experiments showed that the algorithm has a precision rate of 94.29%, a recall rate of 94.07%, and an F1 value of 94.18%. The F1 value is 1.54% higher than Faster R-CNN and 3.45% higher than Yolov3. It takes 58 ms on average to recognize an image, which provides a theoretical basis for the fruit detection. Practical Applications Fruit picking is a labor-intensive task. Traditionally, fruit picking relies on manpower. This method of harvesting has high cost and low efficiency, which seriously hinders the development of the fruit industry. This research uses deep learning algorithms to detect and recognize cherry tomatoes, guide robots in picking, and improve production efficiency. It is of great value to the recognition technology of industrial-scale fruit picking robots.