HEC-YOLO: A lightweight industrial surface defect detector with a feature-preserving backbone and context-guided fusion

Deploying accurate defect detectors on resource-constrained edge devices requires balancing computational cost and feature representation quality. To address this challenge, we propose HEC-YOLO, a lightweight detector for tiny and low-contrast industrial surface defects. The backbone is redesigned with a Multi-Scale Convolution Block (MSCB), which combines grouped multi-receptive-field processing and channel shuffle for efficient scale-adaptive feature extraction, and an Efficient Coordinate Attention Block (ECAB), which reconstructs C2f-style aggregation through lightweight feature transformation and structural re-parameterization. Within ECAB, the proposed Global-Coordinate Synergistic Attention (GCSA) jointly models global channel dependencies and orthogonal coordinate-aware information to enhance defect localization. In the neck, a Context-Guided Feature Pyramid Network (CGFPN) integrates an efficient fusion topology, an SPD-Conv-based shallow-detail pathway, and complementary inter-layer and same-layer context fusion. Experiments on NEU-DET and a self-built production-line backlight panel defect dataset show that HEC-YOLO reduces the parameters and FLOPs of YOLOv8n by 42.9% and 50.6%, respectively, while improving mAP@0.5 by 2.1 and 2.8 percentage points on the two datasets. On Jetson AGX Orin, it reaches 70.1 FPS while reducing per-image energy consumption and GPU temperature rise by 30.95% and 21.87%, respectively, relative to YOLOv8n.