SC-PointLSTM: point cloud target recognition framework for linear array push-broom laser fuze

Laser detection and point cloud processing are key elements of measurement, autonomous driving, and defense technology, particularly for high-precision target recognition and environmental sensing. To meet strict requirements for speed and accuracy in target recognition for laser imaging fuzes, this paper presents a new attempted approach named the SC-PointLSTM framework, which is an innovative, rapid target recognition algorithm designed for linear array push-broom laser fuzes. SC-PointLSTM integrates an enhanced PointNet for efficient sparse point cloud processing with a double-layer long short-term memory (LSTM) for temporal feature fusion, addressing the computational challenges inherent to real-time dynamic environments. This approach enables parallel processing to generate linear 3D point clouds from linear push-broom sensors and effectively fuses previously acquired timestamp sequences, resulting in real-time, accurate target recognition at the end of the push-broom scanning process. The robustness and efficiency of the SC-PointLSTM framework were evaluated on both simulated datasets and real-world targets. The results demonstrated competitive results, achieving an <i>F_score</i> exceeding 0.8, with an average recognition time of approximately 32 ms. The SC-PointLSTM framework provides a robust and scalable solution for high-speed target recognition in dynamic environments and real-time laser detection.