Defect recognition network for optical fiber cables based on feature information compensation

Abstract

The occurrence of electrical corrosion defects in ADSS optical fiber cables presents a significant challenge to the reliable operation of communication lines. Despite the importance of this issue, there has been limited research on accurately detecting electrical corrosion defects in recent years. Moreover, existing defect detection algorithms for industrial issues, such as electrical corrosion in ADSS optical fiber cables, are prone to feature information loss. To address this, we propose an improved Feature Compensation You Only Look Once (FC-YOLO) algorithm for effective detection of electrical corrosion defects in optical cables. First, we proposed the Feature Information Compensated Fusion Network (FICFN), which compensates for fusion features, mitigates the loss of defect information during cross-layer fusion, and enhances feature fusion. Second, an auxiliary training head is integrated into the head network, improving the information expression capability of the FICFN. Finally, an Efficient Local Attention (ELA) mechanism is incorporated into the neck network to boost the localization capabilities of the FICFN. To evaluate the efficacy of the proposed FC-YOLO, we conducted comparison experiments using different mainstream algorithms on both the ADSS electrical corrosion defects dataset and the NEU-DET dataset. Results from the ADSS dataset show that, compared to the YOLOv10s algorithm, the proposed algorithm achieves a 4.7 % increase in mean average precision (mAP@50), reaching 90.2 %, and a 4.1 % improvement in mAP@50–95. These enhancements meet the specifications required for power inspection. On the NEU-DET dataset, the algorithm improved mAP@50 and mAP@50–95 by 8.0 % and 6.1 %, respectively, demonstrating its adaptability for industrial defect detection tasks.