Optimizing foreign fiber segmentation performance with DeepLab V3+ and GAN in industrial IoE environments

In industrial Internet of Everything (IoE) environments, the precise detection of tiny foreign fibers on the surface of bobbin yarns is crucial for ensuring the quality of textile products. However, detecting these fibers often exceeds the capabilities of both human vision and existing automation equipment. To address this challenge, this research proposes a novel foreign fiber segmentation method that integrates Generative Adversarial Networks (GANs) with an enhanced encoder-decoder architecture, significantly improving detection accuracy in industrial IoE scenarios. Specifically, we develop a dual-path attention encoding network that synergistically combines MobileNetV2's computational efficiency with ContextNet's multi-scale contextual awareness, thereby enhancing the extraction of contextual features for tiny foreign fibers. A hybrid channel-spatial attention mechanism is designed by parallel integration of channel-wise excitation and spatial attention mapping, which substantially strengthens the capture of discriminative features for tiny foreign fibers in high-resolution images. The decoding stage employs dense skip-connections to construct multi-scale feature propagation paths, optimizing detail preservation during upsampling processes. To tackle the severe class imbalance in fiber-background pixel distribution, this research introduces a Weighted Binary Cross-Entropy (WBCE) loss function with adaptive focal weighting. Experimental results demonstrate that the proposed DeepLab-DPA framework achieves 98.77% Accuracy, 85.93% MIoU, and balanced performance metrics (87.01% Precision, 86.84% Recall, 86.92% F1-Score), confirming its effectiveness for industrial fiber detection tasks.