Visual detection of sonar images is a critical technology in complex water resource exploration and underwater foreign object target detection. Aiming at the problems of weak features and background information interference of small targets in sonar images, we propose a weak feature confocal channel modulation algorithm for underwater sonar target detection. First, in order to improve the model's ability to capture and characterize the information of weak targets, we design a weak target-specific activation strategy and introduce an a priori frame scale calibration mechanism to match the underlying semantic feature detection branch to improve the accuracy of small target detection; second, we apply the global information aggregation module to deeply excavate the global features of weak targets to avoid the redundant information from covering the small target's weak key features; finally, in order to solve the problem of traditional spatial pyramid pooling which is easy to ignore the channel information, the confocal channel regulation pooling module is proposed to retain effective channel domain small target information and overcome interference from complex background information. Experiment results show that the model in this paper achieves an average detection accuracy of 83.3% on nine types of weak targets in the underwater sonar dataset, which is 5.5% higher than the benchmark. Among these, the detection accuracy of iron buckets, human body models and cubes is significantly improved by 24%, 8.6%, and 7.3%, respectively, effectively solving the problem of leakage and misdetection of weak targets in complex underwater environment.