Three-dimensional object detection using LiDAR has attracted significant attention due to its resilience to lighting conditions and ability to capture detailed geometric information. However, existing methods still face challenges, such as a high proportion of background points in the sampled point set and limited accuracy in detecting distant objects. To address these issues, we propose semantic-guided proposal sampling-RCNN (SPS-RCNN), a multi-stage detection framework based on point–voxel fusion. The framework comprises three components: a voxel-based region proposal network (RPN), a keypoint sampling stream (KSS), and a progressive refinement network (PRN). In the KSS, we propose a novel semantic-guided proposal sampling (SPS) method, which increases the proportion of foreground points and enhances sensitivity to outliers through multilevel sampling that integrates proposal-based local sampling and semantic-guided global sampling. In the PRN, a cascade attention module (CAM) is employed to aggregate features from multiple subnets, progressively refining region proposals to improve detection accuracy for medium- and long-range objects. Comprehensive experiments on the widely used KITTI dataset demonstrate that SPS-RCNN improves detection accuracy and exhibits enhanced robustness across categories compared to the baseline.

Given the harsh working conditions of underwater pan-tilt systems, their energy consumption management is particularly crucial. This study proposes an underwater pan-tilt operation method with an automatic wake-up mechanism, which activates only upon target detection, replacing conventional timer-based triggering. Furthermore, departing from fixed-duration observation strategies, we introduce a Q-learning algorithm to optimize operational modes. The algorithm dynamically adjusts working modes based on surrounding biological activity frequency: employing a low-power mode (reduced energy consumption with lower monitoring intensity) during periods of sparse biological presence and switching to a high-performance mode (extended observation duration, higher energy consumption, and enhanced monitoring intensity) during frequent biological activity. Simulation results demonstrate that compared to fixed-duration observation schemes, the proposed optimization strategy achieves a 11.11% improvement in monitoring effectiveness while achieving 16.21% energy savings.

Underwater fish detection and multi-object tracking support marine ecological monitoring, underwater robot inspection, and fish behavior analysis. In a tracking-by-detection framework, this study targets three specific problems: unstable detection inputs for small- and medium-scale elongated fish, short-term trajectory breaks when low-confidence true detections are discarded, and identity switches caused by local candidate-edge competition when fish cross or move close together. We address these problems with a joint framework that combines the FishDet-XR detector and the FishBoT-SLR-TDR tracker. FishDet-XR stabilizes detector inputs through the proposed SFP-AFPN-XR feature-fusion neck, strip-shaped directional feature modeling, the Simple Parameter-Free Attention Module (SimAM), and positive-prior sampling. FishBoT-SLR-TDR improves tracker-side association through Spatially-Gated Low-Score Recovery (SLR) and Trajectory-Direction Reranking (TDR). Experiments on BrackishMOT-onlyfish show that the framework improves detection localization and tracking continuity while maintaining real-time inference. At the detection stage, FishDet-XR improves mean average precision at an Intersection over Union threshold of 0.50 (mAP₅₀) from 74.48% to 76.19%, mean average precision averaged over Intersection over Union thresholds from 0.50 to 0.95 (mAP_50–95) from 40.72% to 43.55%, and Precision from 86.75% to 88.00% compared with YOLO11s-640, while maintaining 54.76 frames per second (FPS). Compared with the YOLO11s-640 + BoT-SORT baseline, the final detection-tracking chain increases Higher Order Tracking Accuracy (HOTA) from 40.090 to 42.497 and identity F1 score (IDF1) from 51.372 to 54.926, while reducing identity switches (IDSW) from 184 to 159 and trajectory fragmentations (Frag) from 270 to 253.