Road Junction Obstacle Detection With Dual AI Image Verification

The patent combines multiple detection technologies (radar for detection range and optical cameras for classification) into a unified obstacle detection system. The radar component provides long-range detection capability while the optical camera component provides detailed object classification, merging the strengths of both technologies to resolve the contradiction between detection range and classification precision.

The patent implements a feedback mechanism where the system continuously monitors detection results and adjusts its operation based on environmental conditions. The dual-model approach (detection model and absence model) provides feedback validation, where the absence model verifies negative detections to reduce false positives, and the system adapts to environmental changes to maintain precision while preserving detection capability.

The patent employs optical cameras as a universal detection medium that can detect all types of objects (metal, non-metal, organic, inorganic) through their visual characteristics. The deep learning model is trained to recognize diverse object types, making the system multi-functional and versatile while maintaining high detection accuracy across different material types, thereby replacing the limited metal-loop approach.

The patent implements dynamic adaptation to lighting conditions through the deep learning model, which can adjust its detection parameters and characteristics based on environmental illumination levels. The system maintains continuous monitoring operation while dynamically optimizing detection precision for current lighting conditions, allowing it to function effectively across varying light levels from bright daylight to dark nighttime conditions.