Although the work of identifying animal and plant objects with highly similar patterns (e.g., texture, intensity, colour, etc.) to the background has recently attracted more research interest but rarely involves the military complex environment. Design an efficient camouflage small-scale object detection algorithm that is capable of quickly discriminating the objects in open scenes from a long distance, to pre-empt the enemy. In this work, we first recognize the fact that existing open-source training datasets are scarce, and we have created a specific disguised people benchmark covering multiple scenes and weather conditions. Second, because the severe corruption of camouflage capabilities and chaotic scenes in the open battlefield on detailed features and generalization intensifies the challenge of feature extraction from a long-range perspective, we propose a novel end-to-end Small-scale open scene Camouflage Object Detection Network, called SM-CODN. Inspired by the characteristics of biological brain partition, a multi-domain partition module (MPM) with domain-decoupling is proposed to enable specific knowledge learning for samples with obvious discrepancies in camouflage domain distribution. Concurrent with our work, we have designed a multi-scale fusion module (MFM) to strengthen the semantic features related to small-scale disguised objects. Moreover, due to the convergence direction of the detector in reasoning being inconsistent, a feature separation enhancement module (FSEM) is also proposed. Experimental results show that SM-CODN surpasses many classic object detection methods and shows strong competitiveness compared with state-of-the-art ones.