In the domain of printed circuit board (PCB) defect detection and classification, the availability of diverse and comprehensive datasets is fundamental for developing effective detection models. However, existing datasets often lack comprehensive labeling and focus on specific defect types, limiting their applicability to real-world scenarios. To address this gap, we introduce a new dataset named ‘dataset for Mixed Defect Detection in PCB’ (MDD_PCB), which includes intentionally induced mixed PCB defects to provide a more realistic representation of practical scenarios. We evaluate the MDD_PCB dataset using YOLO models and implement it successfully for real-time inference on Jetson Nano, achieving enhanced detection capabilities. Our optimized YOLOv5n model trained on the MDD_PCB dataset achieves impressive metrics (accuracy 93%, precision 95%, recall 96%, mAP 95%, F1-score 94%) with a detection speed of 120.69 frames per second (FPS). Real-time deployment on the Jetson Nano demonstrates practical usability with a detection speed of 30 frames per second (FPS). These results underscore the significance of the diverse dataset proposed, which contributes to robust detection solutions and advances in PCB defect detection methodologies.

This paper critically examines the allocation of liability when autonomous artificial intelligence (AI) systems cause accidents. Problems of applying existing principles of legal liability in AI environment are addressed. This paper argues that the sharing of risk as a basis for proportionate liability should be a basis for a new liability regime to govern future autonomous machines. It is argued that this approach favors the reality of parties’ consent to taking the risk of unpredictable AI behavior over the technicality of existing principles of legal liability. The suggested approach also encourages transparency and responsible decisions of developers and owners of AI systems. A flowchart to clarify possible outcomes of applying the suggested approach is provided. The paper also discusses the need for harmonization of national laws and international cooperation regarding AI incidents crossing national borders to ensure predictability of legal rules governing the liability ensuing from AI applications.