Abstract: Aiming at the problem that apple fruit was easily blocked by branches, leaves, and other obstacles in the natural environment, which led to the reduction of recognition accuracy, a method of apple fruit detection based on the fusion of generative adversarial networks （GAN） was introduced. The Faster RCNN model was used to detect the apple fruit and occlusion, mask the occluded apple fruit image, and then repair the occluded apple fruit image with the generative adversarial networks. Finally, the repaired image was transmitted to the target detection model for identification and positioning. The results showed that the combined model of GAN-Faster RCNN, which fused generative adversarial networks, had an mAP of 73.62% on the test set for apple fruits with a large area of occlusion, which was 8.76 percentage points higher than the original model; for the apple fruit with a small area of occlusion, the average precision on the test set was 90.67%, which was 9.54 percentage points higher than the original model. It solved the problem of low accuracy of apple fruit recognition under occlusion conditions with traditional target detection methods.

Key words: apple, target detection, occlusion, Faster RCNN, generative adversarial networks（GAN）