Aiming to solve the problem of pavement crack detection in complex road environments, an improved algorithm based on YOLOv5s is proposed. First, the CBAM (Convolutional Block Attention Module) is introduced after the backbone network’s C3 modules to focus the complex scene’s practical information and enhance the model’s attention to the crack region. Second, the BiFPN (Bidirectional Feature Pyramid Network) is used in Neck to replace the bidirectional PANet (Path Aggregation Network) in YOLOv5s to improve the multiscale feature fusion, which reduces detection leakage due to illumination and scale factors. Thirdly, the P6 detection head for denser cracks is added to the Head, and the CA (Coordinate Attention) module is introduced to improve the crack detection capability at multiple scales. Finally, the algorithm is experimentally compared with SSD (Single Shot MultiBox Detector), Faster-RCNN (Faster-Region Convolutional Neural Network), and unimproved YOLOv5s on the constructed pavement crack image dataset, and the results show that compared with other algorithms, when mAP@0.5 (mean Average Precision when IoU=0.5) values are improved by 10.6%, 9.2%, and 4.6%, respectively. Cracks are identified better than the other three models.

Simultaneous Localization and Mapping (SLAM) enables mobile robots to autonomously perform localization and mapping tasks in unknown environments. Despite significant progress achieved by visual SLAM systems in ideal conditions, relying solely on a single robot and point features for mapping in large-scale indoor environments with weak-texture structures can affect mapping efficiency and accuracy. Therefore, this paper proposes a multi-robot collaborative mapping method based on point-line fusion to address this issue. This method is designed for indoor environments with weak-texture structures for localization and mapping. The feature-extraction algorithm, which combines point and line features, supplements the existing environment point feature-extraction method by introducing a line feature-extraction step. This integration ensures the accuracy of visual odometry estimation in scenes with pronounced weak-texture structure features. For relatively large indoor scenes, a scene-recognition-based map-fusion method is proposed in this paper to enhance mapping efficiency. This method relies on visual bag of words to determine overlapping areas in the scene, while also proposing a keyframe-extraction method based on photogrammetry to improve the algorithm’s robustness. By combining the Perspective-3-Point (P3P) algorithm and Bundle Adjustment (BA) algorithm, the relative pose-transformation relationships of multi-robots in overlapping scenes are resolved, and map fusion is performed based on these relative pose relationships. We evaluated our algorithm on public datasets and a mobile robot platform. The experimental results demonstrate that the proposed algorithm exhibits higher robustness and mapping accuracy. It shows significant effectiveness in handling mapping in scenarios with weak texture and structure, as well as in small-scale map fusion.

Energy efficiency and balanced load are essential for the development of clustering algorithms for Mobile Wireless Sensor Networks (MWSNs). Aiming at the problems of uneven load distribution and high overhead of command interaction in existing clustering schemes, we propose an efficient low-energy balanced clustering based on fast Voronoi diagram division (ELBCFVD) algorithm. The innovation of this algorithm is that by judging the information value of the data acquired by the nodes, selecting the representative nodes that satisfy the fast solution of the Voronoi diagram division scheme, optimizing the reconfiguration process of the clusters together with the correspondence of the stable workflows in the mobile scenarios, effectively reducing the overall amount of interaction of the control commands, and realizing the redistribution of the network’s energy consumption and balancing the routing loads of various parts of the network. Detailed simulation analysis shows that the proposed ELBCFVD algorithm significantly reduces the frequency of cluster reconstruction and cluster head rotation, balances the communication load among clusters, and achieves 104.4%, 45.8%, and 15.4% network lifetime extension compared to LEACH-M, EEMCS, and HLBC algorithms, respectively.