This study addresses the situational awareness requirements for unmanned surface vehicles (USVs) operating in nearshore environments by proposing a dual solid-state LiDAR-based method for detecting water surface targets. The proposed method achieves high-resolution detection at a relatively low cost. First, a joint calibration model for the two solid-state LiDARs is developed utilizing the least squares method to determine the extrinsic parameters of the LiDAR scanning system. This calibration broadens the horizontal field of view while maintaining a 144-line scanning resolution. Second, a joint calibration between the LiDAR scanning system and the inertial measurement unit is performed, which reduces point cloud distortion through pose calibration and corrects scanning errors caused by the vessel's motion. In the detection algorithm, adaptive kernel convolution and hierarchical attention mechanisms are integratedinto the CenterPoint network, resulting in significant improvements in both inference speed and detection accuracy. A dedicated dataset for nearshore situational awareness is created, and experimental validation is conducted. Results show that the proposed algorithm improves overall average precision for bird's eye view (BEV AP) and 3D AP by 6.14 percentage points and 4.66 percentage points, respectively, and increases inference speed by more than 40% as compared with the baseline algorithm.