A lightweight fruit detection based on improved YOLOv5s

Hongxin Liu; Shenming Gu; Ying Cao; Junlin Li

doi:10.1117/12.3051426

18 November 2024 A lightweight fruit detection based on improved YOLOv5s

Hongxin Liu, Shenming Gu, Ying Cao, Junlin Li

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134033S (2024) https://doi.org/10.1117/12.3051426
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

Aiming at the low efficiency of manual input of electronic scales in supermarkets, wasting labor costs and unable to adapt to the current technological development, this study therefore introduces a lightweight detection of fruits based on the improved YOLOv5s for electronic scales in supermarkets to achieve fast and accurate identification of relevant fruit targets. First, the dataset is obtained from the publicly available fruit dataset and organized and then manually labeled, mainly labeling the location and category of the fruit. Second, the backbone of YOLOV5s uses MobileNetV3 to reduce parameters for easy deployment in mobile devices. Then, BiFPN is introduced in the process of neck cross-scale fusion, which can be able to fuse multi-scale features more effectively and further improve the accuracy to the network. Finally, to further increase the precision of anchor boxes, the CA coordinate attention mechanism is introduced before the head prediction. The experiment shows that the improved YOLOv5s improves 17.3,12,10.3,and 1.2 percentage points compared with SSD, YOLOV3, YOLOv4, and YOLOv5s respectively, and the size of the improved YOLOv5s has decreased from 13.8MB to 7.2MB, and the improvement of YOLOv5s in this experiment can be better applied and deployed for the superstore's electronic scales and better meet the practical needs.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongxin Liu, Shenming Gu, Ying Cao, and Junlin Li "A lightweight fruit detection based on improved YOLOv5s", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134033S (18 November 2024); https://doi.org/10.1117/12.3051426

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KEYWORDS

Target detection

Feature fusion

Education and training

Ablation

Performance modeling

Visual process modeling

Data modeling

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