Dual-Band Asymmetry-Guided Long-Range Cat-Eye Recognition with Multi-Scale Fusion

At detection ranges on the order of kilometers, cat-eye echoes degrade into pixel-level spots and exhibit responses similar to those of compact, high-echo false targets, making it difficult for single-band systems to balance weak-target detection rates with false alarm control. To address this, this paper pairs spatially registered and radiometrically calibrated 808 nm and 905 nm images to form a dual-band detection pair, utilizing the cross-band response asymmetry caused by chromatic defocus for discrimination. Experiments show that cat-eye targets exhibit coupled asymmetry in intensity, scale, and energy distribution, whereas the false targets under test generally maintain approximate symmetry; furthermore, the dominant discriminative information shifts from scale differences to intensity differences as distance increases. Based on this pattern, this paper proposes a range-conditioned “detection–reclassification” framework that dynamically matches appropriate models based on target distance. In this framework, a structurally symmetric, two-branch, multi-scale attention-fused YOLO network is used for candidate target detection, while an explicit asymmetric feature classifier further processes challenging samples to retain weak targets and suppress false alarms. Field experiments conducted at distances ranging from 100 to 2100 m achieved an overall mAP50 of 0.907 and an overall mAP50–95 of 0.475, with the mAP50 improving by 17.7 and 10.8 percentage points, respectively, compared to the 808 nm and 905 nm single-band baselines. The results demonstrate that this framework can effectively balance the detection of weak targets at long ranges with the suppression of false targets under the evaluated conditions.