Defect detection on Printed Circuit Boards (PCBs) constitutes a pivotal component of the quality control system in electronics manufacturing. However, owing to the intricate circuitry structures on PCB surfaces and the characteristics of defects—specifically their minute scale, irregular morphology, and susceptibility to background texture interference—existing generic deep learning models frequently fail to achieve an optimal equilibrium between detection accuracy and inference speed. To address these challenges, this study proposes MDEB-YOLO, a lightweight real-time detection network tailored for PCB micro-defects. First, to enhance the model’s perceptual capability regarding subtle geometric variations along conductive line edges, we designed the Efficient Multi-scale Deformable Attention (EMDA) module within the backbone network. By integrating parallel cross-spatial channel learning with deformable offset networks, this module achieves adaptive extraction of irregular concave–convex defect features while effectively suppressing background noise. Second, to mitigate feature loss of micro-defects during multi-scale transformations, a Bidirectional Residual Multi-scale Feature Pyramid Network (BRM-FPN) is proposed. Utilizing bidirectional weighted paths and residual attention mechanisms, this network facilitates the efficient fusion of multi-view features, significantly enhancing the representation of small targets. Finally, the detection head is reconstructed based on grouped convolution strategies to design the Lightweight Grouped Convolution Head (LGC-Head), which substantially reduces parameter volume and computational complexity while maintaining feature discriminability. The validation results on the PKU-Market-PCB dataset demonstrate that MDEB-YOLO achieves a mean Average Precision (mAP) of 95.9%, an inference speed of 80.6 FPS, and a parameter count of merely 7.11 M. Compared to baseline models, the mAP is improved by 1.5%, while inference speed and parameter efficiency are optimized by 26.5% and 24.5%, respectively; notably, detection accuracy for challenging mouse bite and spur defects increased by 3.7% and 4.0%, respectively. The experimental results confirm that the proposed method outperforms state-of-the-art approaches in both detection accuracy and real-time performance, possessing significant value for industrial applications.

Automobile sales analysis provides an important basis for production planning, procurement, supply-chain coordination, inventory management, and dealership resource allocation. However, dealership sales are jointly influenced by operational practices, market conditions, and regional policies. Conventional machine-learning models may have difficulty capturing spatial heterogeneity, maintaining predictive performance under regional sample imbalance, and providing interpretable evidence for managerial analysis. To address these issues, this study constructs a multi-source automotive dealership dataset based on automaker marketing records and proposes CDMEP, a multi-paradigm framework integrating spatial diagnosis, ensemble transfer learning, predictive interpretation, and assumption-conditional structural diagnosis. The framework comprises a Spatial Heterogeneity Decoupling Diagnostic Model (SHDM) for examining spatial clustering and regional differences; a Regional Adaptive Stacked Learner (RASL) combining balanced cross-regional transfer, TPE-tuned base learners, and constrained ensemble weighting; and a Causally Scrutable Optimization Policy (CSOP) incorporating SHAP-, GAM-, and PDP-based predictive interpretation, temporally ordered LiNGAM diagnosis, edge-level stability and sensitivity analysis. The results indicate the following: (1) dealership sales exhibit spatial clustering and regional heterogeneity in variables related to lead generation, customer conversion, and dealership service capability; (2) RASL achieves an R² of 0.875 on the independent test set, corresponding to an absolute improvement of 0.051 over the second-best benchmark in the present dataset; and (3) under the specified preprocessing procedures and LiNGAM assumptions, lead volume, customer traffic, and conversion rate repeatedly appear in candidate directional structures associated with next-month sales, although their estimated coefficients and diagnostic stability vary across regions. These findings provide preliminary evidence that CDMEP may serve as a useful analytical framework for regionally differentiated sales prediction and assumption-aware dealership analysis.