Automated Defect Inspection in Casting X-ray Images: A DeepLabv3+ Framework with Adaptive Receptive Field and Multi- scale Fusion

In the casting DR ray defect detection, the defective region usually has similar grey scale characteristics with the background, and the defect size span is large, which makes it difficult to accurately segment the defective region by the traditional detection method, thus affecting the detection accuracy and efficiency. For the casting defect detection semantic segmentation algorithm has the problems of low segmentation accuracy and large arithmetic capacity, DeepLabv3 + is selected as the baseline model, and targeted optimisation and improvement is carried out on the basis of this algorithm. In order to solve the problem of imbalance between positive and negative samples of data, we propose a hybrid loss function WCE-Dice Loss that combines the weighted cross-entropy loss function and the Dice loss function; we replace Xception, the backbone network, with MobileNetv2 to lighten the network architecture, and we add the ECA attention mechanism to the MobileNetv2 backbone network to enhance the model's ability to capture defective features; we integrate DeepLabv3 + as the baseline model and optimise and improve it on the basis of the algorithm. defective features; integrating the CFF multi-scale feature fusion structure to better extract contextual information, as well as comprehensively considering the dense connectivity idea and the need for lightweight improvement, the D_DenseASPP module structure is proposed. Experiments show that the improved DeepLabv3 + model improves 5.76% in mIoU index and 6.17% in Dice coefficient, and the number of parameters is only 11.81% of the original DeepLabv3 + model, which is an effective balance between model segmentation accuracy and computational complexity, and is suitable to be deployed in real industrial environments. The research in this paper provides an efficient and accurate solution for casting DR ray defect detection, which has important industrial application value and provides strong technical support for subsequent automated inspection and process optimisation.