CamLite: accelerating precise anomaly detection for camshaft on edge devices

Camshafts, mechanical transmission devices with highly reflective surfaces, are widely utilized in the automotive industry. To ensure their performance and longevity, comprehensive quality inspections must be performed to identify any minor defects. Although deep learning has achieved success in industrial quality inspection applications, high computational power costs have impeded its progress. To address this limitation, this study proposed CamLite, a lightweight model for anomaly detection on edge devices. CamLite incorporates three key innovations: a lightweight backbone network, an auxiliary learning paradigm, and a dynamic weighted average strategy. The lightweight backbone network, a straight-through network constructed with reparameterization blocks, efficiently extracts detailed and semantic features with minimal parameter and memory overhead. The auxiliary learning paradigm improves the model's performance by incorporating an anomaly classification task with higher information entropy without increasing inference latency. Moreover, the dynamic weighted average strategy adjusts the loss weight of the primary and auxiliary tasks, allowing the network optimization process to focus more on minimizing the loss of the primary task. Experimental results demonstrate that CamLite achieves an outstanding balance between accuracy, model size, and latency, with a Matthews correlation coefficient of 0.924 and only 0.63M parameters.