Goggle Wear Detection Algorithm based on Improved Faster R-CNN
Pages 416 - 423
Abstract
Aiming to address the issues of insufficient model training, low detection accuracy, and poor generalization capabilities caused by the low quality of the current goggles-wearing detection dataset, insufficient samples, and imbalanced sources, a goggles-wearing detection dataset that includes a variety of real and complex scenarios was constructed. An improved object detection algorithm based on Faster R-CNN was proposed to detect the wearing of protective goggles. This algorithm improves upon the Faster R-CNN by refining a more reasonable loss function, solving the problem of ineffective calculation of loss under special circumstances. At the same time, PAFPN is used to replace the original FPN, allowing the detection model to have a bottom-to-up secondary fusion, effectively enhancing feature extraction capabilities. Experimental results on the goggles-wearing detection dataset indicate that the improved Faster R-CNN model has an average precision of 82.7%. Compared to the Faster R-CNN model, the average precision has increased by 3.8%, enabling the detection of goggles wearing in complex environments.
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Index Terms
- Goggle Wear Detection Algorithm based on Improved Faster R-CNN
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Published In
January 2024
480 pages
ISBN:9798400716720
DOI:10.1145/3647649
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Published: 03 May 2024
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ICIGP 2024
ICIGP 2024: 2024 the 7th International Conference on Image and Graphics Processing
January 19 - 21, 2024
Beijing, China
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