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7 December 2023 Recognition and segmentation of apple fruits in natural environments
Jialin Zhang, Yuxin Pan
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Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129413A (2023) https://doi.org/10.1117/12.3011503
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China
Abstract
By improving the Mask R-CNN model, the performance of the model is improved for recognizing and segmenting apples in the orchard that are in natural environments such as different light, different weather and overlapping occlusion. Firstly, the dual-channel SE module is introduced into the feature extraction network, secondly, the anchor frames are modified according to the actual exposed proportion and area of the apples, and finally, the NMS is improved to Soft-NMS to enhance the model's recognition ability for apple identification and segmentation in natural environments. The results of the experiments using apple images from real orchard environments: Pixel Acc was 98.4%, mean intersection and Mean IoU was 78.8%, class intersection and Class IoU was 59.4%, and Class Mean Acc was 84.8%. The results show that the improved model in this paper gives better results than the original Mask R-CNN model for apple recognition and segmentation, as well as some other models that are widely used.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Jialin Zhang and Yuxin Pan "Recognition and segmentation of apple fruits in natural environments", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129413A (7 December 2023); https://doi.org/10.1117/12.3011503
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KEYWORDS
Image segmentation
Feature extraction
Target detection
Deep learning
Agriculture
Target recognition
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