Application of Deep Learning-Based Multi-Scale Feature Fusion in the Visual System of Precision Welding Robots

In high-end equipment manufacturing and aerospace industries, the quality of precision welding directly affects product reliability.However, the welding process is often challenged by complex lighting variations, metal spatter, torch occlusion, and multi-scale defect characteristics, which pose significant difficulties for defect detection in robotic visual systems in terms of both accuracy and real-time performance.Traditional handcrafted feature methods and early deep learning models suffer from insufficient utilization of multiscale features and inadequate fusion of contextual semantics, resulting in high missed detection rates of small defects and failures in occluded scenarios.Existing single-scale feature networks tend to overlook low-level detail information, and conventional feature fusion methods fail to fully exploit cross-resolution feature complementarity.In addition, fixed anchor box schemes lead to high localization errors, and the lack of online compensation mechanisms for dynamic occlusions hinders detection performance in realworld applications.To address these challenges, this paper proposes a real-time welding defect detection method based on multi-resolution feature fusion tailored for the visual system of precision welding robots.The research encompasses six key aspects: data acquisition, optimization of the detection network, backbone network enhancement, multilayer feature fusion, adaptive anchor box adjustment, and occlusion-aware stereo vision measurement.By constructing a diverse multi-condition dataset, introducing cross-layer attention mechanisms, and designing an adaptive feature fusion strategy along with a spatiotemporal joint compensation model, the proposed method effectively overcomes the limitations of single-scale feature dependence.Experimental results demonstrate significantly improved detection accuracy for multi-scale defects under complex conditions and enhanced adaptability in dynamic scenes.The outcomes of this study offer a reusable technical framework for industrial visual inspection and provide meaningful contributions toward the intelligent development of precision welding.