Tomato recognition and location algorithm based on improved YOLOv5

In order to meet the requirements of target detection and location for intelligent tomato picking, a recognition and location algorithm based on improved YOLOv5s is proposed in this paper. The CARAFE module structure was used to optimize the upsampling algorithm of YOLOv5s, which increased the receptive field of the network while maintaining the lightweight. EIoU and Quality Focal Loss were introduced to optimize the loss function of the network, which solved the problem of reduced accuracy caused by uneven samples, and at the same time accelerated the training convergence speed and improved the regression accuracy. The improved model is denoted as YOLOv5s-CQE. Compared with YOLOv5s, the mAP_0.5 and mAP_0.5:0.95 of YOLOv5s-CQE was increased by 1.67 and 3.43 percentages points, respectively. The recognition accuracy of YOLOv5s-CQE model in the test set was 99.77%, which had increased by 2.40 percentages points compared with before improvement, and was 4.10, 6.47 and 3.03 percentages points higher than that of the lightweight networks YOLOv4-tiny, YOLOv5-Lite-e and YOLOv5-Lite-s, respectively. Compared with YOLOv7 and Faster RCNN, the recognition accuracy rate was increased by 1.69, 3.97 percentages points respectively. In order to improve the accuracy of positioning, distortion removal and ROI clipping were carried out on the obtained images. The accuracy of location was tested by laboratory positioning test and field picking test. The results showed the total average errors decreased by 6.65 mm compared with those before distortion removal, and the field positioning accuracy was improved by 6.67 percentages points. The experimental results showed that the algorithm in this paper had the advantages of high precision, fast detection speed and strong robustness, which provided a theoretical basis for intelligent tomato picking.