Multi-Scale CNN Object Detection for Distance-Based Confidence Dips

The patent applies local quality by making the detection threshold adaptive rather than uniform. The system divides the image space into different distance zones and assigns different class thresholds to each zone. Objects at distances where confidence dips occur use adjusted thresholds specific to their distance range, while objects at other distances use standard thresholds. This localized adaptation resolves the contradiction by maintaining high reliability across all distances while preserving the multi-scale capability to detect objects of different sizes.

The patent implements dynamics by making the class threshold a dynamic parameter that changes based on object distance. Instead of using fixed thresholds for all objects, the system dynamically selects thresholds from multiple predefined sets corresponding to different distance zones. This dynamic adaptation maintains ease of operation through automated threshold selection while significantly improving measurement precision for objects at distances where confidence dips occur.

The patent resolves this contradiction by applying local quality through distance-specific threshold adjustment. Instead of uniformly increasing the confidence threshold for all objects (which would cause missed detections of distant objects), the system applies adjusted thresholds locally to objects at distances where confidence dips occur. This allows the system to maintain high reliability by reducing incorrect detections at problematic distances while preserving the ability to detect distant objects that would otherwise be missed.