Detection of farm harvestable apples based on improved YOLOv7

Xin Chen; Kun Chen

doi:10.1117/12.2684261

1 August 2023 Detection of farm harvestable apples based on improved YOLOv7

Xin Chen, Kun Chen

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 1275421 (2023) https://doi.org/10.1117/12.2684261
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

To improve the detection performance of overlapping apples, as well as apples occluded by tree branches and leaves, we propose an improved YOLOv7-based method for apple target detection on the MinneApple dataset. Firstly, we introduce the RepLKNet module in the backbone network, which directly enlarges the kernel size to obtain a larger effective receptive field (ERF) and extract more feature information from the images. Secondly, we apply the Wise-IoU loss function and a dynamic non-monotonic Focusing Mechanism strategy to adapt high-quality anchor boxes to low-quality samples, and pay more attention to normal anchor boxes, thereby improving the overall performance of the network model. Compared with current mainstream algorithms, our proposed method has certain advantages, achieving a Mean Average Precision (mAP) of 96.17% and a detection efficiency of 39 frames per second (FPS) on the MinneApple dataset, meeting the real-time detection requirements.

Citation Download Citation

Xin Chen and Kun Chen "Detection of farm harvestable apples based on improved YOLOv7", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 1275421 (1 August 2023); https://doi.org/10.1117/12.2684261

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