<p>As an important image preprocessing method, salient object detection has achieved excellent performance in various computer vision tasks. Moreover, salient object detection under weak observation conditions has deeper research value and application prospects in the fields of assisted driving and autonomous driving. However, existing salient object detection methods are generally designed for high-quality images obtained in conventional good scenes. It is a great challenge for these methods to solve the problem of difficulty in excavating the saliency information of foreground objects in low-quality images caused by occlusion and interference from specific weather background. Therefore, a novel unsupervised adaptive learning method for salient object detection under weak observation conditions is proposed, which generates accurate saliency maps by constructing a pseudo label generation network and a saliency detection network. Specifically, the adaptive learning of filtering parameters module is designed in the pseudo label generation network to augment image quality and assist in excavating saliency information by dynamically remove interference from adverse backgrounds. In addition, the saliency feature grafting module that can fuse low-level and high-level semantic features of images is proposed in the saliency detection network to refine object edges. The experimental results show that the proposed method adaptively detects salient objects under normal and weak observation conditions.</p>