LCA-YOLOv8: lightweight object detection with CCFF and ACMix for safety helmets

Shuangshuang Li

doi:10.1117/12.3049620

8 November 2024 LCA-YOLOv8: lightweight object detection with CCFF and ACMix for safety helmets

Shuangshuang Li

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134162L (2024) https://doi.org/10.1117/12.3049620
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

In practical production processes, wearing a safety helmet is a crucial guarantee of safe production. Helmet detection can effectively reduce the probability of safety accidents. However, existing algorithms often suffer from large parameter quantities, high computational complexity, and poor real-time performance. To address these issues, we propose a lightweight helmet-wearing detection algorithm based on the YOLO v8 framework, named LCA-YOLOv8. We adopt a lightweight CCFF module to reduce the parameters of the neck layer and use the ACMix module to increase network depth and enhance the neural network's ability to capture both shallow and deep semantic information, thereby improving feature representation capability. Experimental results show that on the public SHWD dataset, compared with YOLO v8 baseline, our algorithm not only reduces the parameter count by 30.4% and the floating-point operations by 13.6%, but also achieves a precision of 92.7%, effectively balancing high performance and real-time requirements to meet the needs of actual safe production.

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

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Shuangshuang Li "LCA-YOLOv8: lightweight object detection with CCFF and ACMix for safety helmets", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134162L (8 November 2024); https://doi.org/10.1117/12.3049620

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KEYWORDS

Object detection

Safety

Performance modeling

Detection and tracking algorithms

Convolution

Evolutionary algorithms

Feature fusion

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