Efficient Knowledge-Synthesized Feature Learning for Industrial Food Safety Inspection

Abstract

The rapid advancement of machine learning and computer vision technologies has revolutionized industrial food safety inspection systems. However, traditional approaches often rely on isolated feature extraction methods that fail to capture the complex interdependencies between visual, spectroscopic, and contextual information critical for comprehensive food safety assessment. This paper proposes a novel Knowledge-Synthesized Feature Learning (KSFL) framework that integrates multi-modal sensory data through a hierarchical knowledge synthesis architecture. The proposed methodology combines Convolutional Neural Networks (CNNs) with domain-specific knowledge graphs to enhance feature representation and improve detection accuracy for various food safety hazards including contamination, spoilage, and foreign object detection. Our experimental evaluation on a comprehensive dataset of 15,000 food samples across five industrial processing facilities demonstrates significant improvements in detection accuracy (94.7%) compared to traditional computer vision approaches (87.2%) and conventional machine learning methods (82.1%). The system achieves real-time processing capabilities with an average inference time of 23.4 milliseconds per image, making it suitable for high-throughput industrial applications. Furthermore, the knowledge synthesis approach demonstrates superior generalization across different food categories and environmental conditions, reducing false positive rates by 31% and improving overall system reliability. This research contributes to the advancement of intelligent food safety inspection systems by providing a robust framework for knowledge-guided feature learning that bridges the gap between automated inspection technology and domain expertise.