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In high-risk industrial environments such as tunnel construction, reliable safety helmet detection is critical for preventing head injuries. However, severe illumination inhomogeneity and multi-scale object appearances pose significant challenges to existing detectors due to static anchor designs and the absence of illumination-aware feature learning. This paper proposes AE-LFOG-YOLO, an end-to-end framework that enhances YOLOv8 through dual physics-informed optimizations. The approach integrates an Illumination-Invariant Module (IIM) that employs a dual-path feature decoupling strategy to suppress lighting artifacts within the network backbone. Concurrently, the Adaptive Evolutionary - Light Field Optimized Generation (AE-LFOG) algorithm replaces static anchors with a dynamic evolutionary process guided by local illumination gradients and thin-lens imaging principles, enabling continuous optimization of anchor parameters during training. Evaluated on a real-world tunnel dataset, the method achieves 94.83% mAP@0.5 and significantly improves robustness under challenging illumination variations, as evidenced by a 35.7% extension in effective operating range. These results demonstrate the effectiveness of integrating physical imaging priors into deep learning for robust visual perception in complex industrial scenarios.

In today’s textile industry, the classification of woven fabrics is usually manual which requires considerable human efforts and a long time. With the rapid development of computer vision, the automatic and efficient methods for woven fabric classification are desperately needed. This paper proposes an automatic and real-time classification method to analyze three woven fabrics: plain, twill and satin weave. The methodology involves two approaches to extract texture features, that is, gray-level co-occurrence matrix (GLCM) and Gabor wavelet. Then, principal component analysis (PCA) is utilized to deal with the texture feature vectors to gain minimize redundancy and maximize principal component feature vectors. Finally, in the classification phase, probabilistic neural network (PNN) is applied to classify three basic woven fabrics. With strong realtime, fault-tolerance and non-linear classification capability, PNN can be a promising tool for classification of woven fabrics. The experimental results show that PNN classifier with faster training speed can classify woven fabrics accurately and efficiently. Besides, compared with GLCM method and Gabor wavelet method, the fusion of the two feature vectors obtains the best classification result (95 %).