Resource-Efficient Surface Defect Detection on Edge Devices Using a Hybrid Descriptor Framework with Data-Driven Feature Selection

Abstract

Surface defect detection across diverse surface types, such as biological shells, photovoltaic panels, and pavement infrastructure, is essential for industrial quality control and structural health monitoring. Although Deep Learning (DL) models perform well in this domain, high computational and memory requirements limit their deployment on resource-constrained edge devices. To address this, we propose a computationally efficient descriptor-level feature extraction framework combining Local Binary Pattern (LBP), Gabor filters, and Discrete Wavelet Transform (DWT) to capture complementary textural, directional, and frequency characteristics of surface defects. An AdaBoost-driven feature selection strategy reduces the high-dimensional hybrid feature pool to the 25 most discriminative attributes, and SHapley Additive exPlanations (SHAP) analysis is applied to interpret feature contributions. The proposed framework achieved 100% accuracy on the EggCrack dataset and 79.0% accuracy on the ELPV dataset, which is more challenging. On ELPV, it outperformed the MCU-optimized MCUNet-In0 (78.16%) and remained within 4.16 percentage points of EfficientNetV2-B0 (83.16%), while achieving approximately 7577× fewer floating-point operations (FLOPs) and 89× smaller model size. Deployed on a Raspberry Pi 5 CPU, the framework achieved an end-to-end inference time of 37 ms per image, enabling real-time inference. These results indicate that the proposed hybrid descriptor and selection framework offers a favorable accuracy-efficiency trade-off for real-time surface defect detection on CPU-based edge devices.