To address the low detection efficiency of dense emitters in radio environment maps, this paper analyzes two fundamental challenges: feature confusion, which causes merged targets and increases missed detections, and boundary ambiguity, which blurs edges and raises false alarms. Together, they create a precision–recall dilemma. To this end, we propose a Separation and Boundary-Aware Collaborative Enhancement Detection Network, which contains two modules that respectively separate confused features and restore blurred boundaries. Experiments on a dense emitter dataset constructed from RadioMapSeer demonstrate that SBCE-Net achieves an F1 score of 0.988 alongside a recall of 0.991 and a precision of 0.985, outperforming seven existing methods in precision–recall balance. The FPPI–Recall curves show that this advantage holds across a broad range of confidence thresholds, and stratified density tests confirm that the method maintains the highest F1 at every density level, with a variation of only 0.116 from the densest to the sparsest condition. Ablation studies further verify that the two modules contribute complementarily, confirming the effectiveness of the collaborative design.
Zhang et al. (Sat,) studied this question.