Existing studies often lack a systematic solution for an Unmanned Aerial Vehicles (UAV) inspection system, which hinders their widespread application in crack detection. To enhance its substantial practicality, this study proposes a formal and systematic framework for UAV inspection systems, specifically designed for automatic crack detection and pavement distress evaluation. The framework integrates UAV data acquisition, deep-learning-based crack identification, and road damage assessment in a comprehensive and orderly manner. Firstly, a flight control strategy is presented, and road crack data are collected using DJI Mini 2 UAV imagery, establishing high-quality UAV crack image datasets with ground truth information. Secondly, a validation and comparison study is conducted to enhance the automatic crack detection capability and provide an appropriate deployment scheme for UAV inspection systems. This study develops automatic crack detection models based on mainstream deep learning algorithms (namely, Faster-RCNN, YOLOv5s, YOLOv7-tiny, and YOLOv8s) in urban road scenarios. The results demonstrate that the Faster-RCNN algorithm achieves the highest accuracy and is suitable for the online data collection of UAV and offline inspection at work stations. Meanwhile, the YOLO models, while slightly lower in accuracy, are the fastest algorithms and are suitable for the lightweight deployment of UAV with online collection and real-time inspection. Quantitative measurement methods for road cracks are presented to assess road damage, which will enhance the application of UAV inspection systems and provide factual evidence for the maintenance decisions made by road authorities.

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with diverse sensors are costly and unsuitable for mountainous roads, primarily because of the challenging terrain conditions characterized by frequent bends in the road. To address these challenges, this study proposes a customized Unmanned Aerial Vehicle (UAV) inspection system designed for automatic crack detection. This system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method (SWM) is proposed to enhance the autonomous navigation of UAV flights by generating path planning on mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the improved MRC-YOLOv8 algorithm is presented to conduct autonomous crack detection from UAV imagery in an on/offboard module. To validate the performance of our UAV inspection system, we conducted multiple experiments to evaluate its accuracy, robustness, and efficiency. The results of the experiments on automatic navigation demonstrate that our fusion method, in conjunction with SWM, effectively enables real-time route planning in GPS-denied mountainous terrains. The proposed system displays an average localization drift of 2.75% and a per-point local scanning error of 0.33 m over a distance of 1.5 km. Moreover, the experimental results on the road crack detection reveal that the MRC-YOLOv8 algorithm achieves an F1-Score of 87.4% and a mAP of 92.3%, thus surpassing other state-of-the-art models like YOLOv5s, YOLOv8n, and YOLOv9 by 1.2%, 1.3%, and 3.0% in terms of mAP, respectively. Furthermore, the parameters of the MRC-YOLOv8 algorithm indicate a volume reduction of 0.19(×10⁶) compared to the original YOLOv8 model, thus enhancing its lightweight nature. The UAV inspection system proposed in this study serves as a valuable tool and technological guidance for the routine inspection of mountainous roads.

The rapid identification of landslides in mountainous terrain through remote sensing imagery after earthquakes or heavy rainfall is crucial for assessing the severity of the situation and providing timely assistance for post-disaster rescue operations. However, traditional manual interpretation methods and computer vision techniques tend to be inefficient and time-consuming. To address this problem, we propose a novel algorithm called LSI-YOLOv8, which builds upon the lightweight YOLOv8 to enable fast and accurate landslide identification using remote sensing imagery. In this study, we introduce optimization techniques integrated into the YOLOv8 model. Firstly, we incorporate an expandable residual attention Dilation-wise Residual Segmentation (DWRSeg) module to reconstruct its neck structure, reducing parameters and computational load. Additionally, we enhance the positioning ability of the model by replacing the bounding box loss function Complete Intersection over Union (CIoU) with Efficient Intersection over Union (EIoU), optimizing it to improve computing speed. Through comparative experiments with other state-of-the-art models, the accuracy of our LSI-YOLOv8 significantly outperforms Mask-RCNN, YOLOv5, YOLOv7, YOLOv8, and YOLOv9 model, with mAP@0.5 being 9.0%,7.7%, 4.0%, 6.3%, and 2.9% higher than other models, while mAP@0.5 :0.95 is 3.1%,3.6%, 2.7%, 4.8%, and 0.3% higher than others, respectively. Furthermore, the detecting speed of the LSI-YOLOv8 model is 73.2 f/s, which is notably faster by 15.5 f/s, 3.5 f/s, 5.1 f/s, 5.9 f/s, and 4.3 f/s respectively, compared to Mask-RCNN, YOLOv5, YOLOv7, YOLOv8, and YOLOv9. The proposed method provides valuable insights for advancing landslide identification methodologies. The focus of subsequent work will be more on multi-source data fusion, such as Digital Elevation Model (DEM) data and Synthetic Aperture Radar (SAR) data to remove the influence of some background information to improve the accuracy of landslide identification.

The study focuses on identifying fireburning and burnt areas in a large-scale forest fire that occurred in Xintian County, China, in October 2022. To investigate the adaptability of machine learning methods in various scenarios for mapping forest fire areas, this study presents a comparative study on the recognition and mapping accuracy of three machine learning algorithms, namely, Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN), based on Sentinel-1B and 2A imagery. Initially, three sets of pre-fire, during-fire, and post-fire remote sensing data were preprocessed. Various feature parameters from Sentinel-1B and 2A imagery were combined to identify firerelated land cover types. The experimental results revealed that: (i) During the pre-fire period, the SVM method demonstrated superior accuracy compared to the other two methods. The combination of spectral and Normalized Difference Vegetation Index (NDVI) features achieved an optimal accuracy for identifying forest areas with an overall accuracy (OA) of 93.52%. (ii) In the during-fire period, RF method exhibited higher accuracy compared to the other two methods with peak fire identification accuracy reached by combining spectral and Normalized Burn Ratio (NBR) index features at an OA of 95.43%. (iii) In the post-fire period, SVM demonstrated superior accuracy compared to other methods. The highest accuracy of 94.97% was achieved when combining spectral and radar features from Sentinel-1B imagery, highlighting the effectiveness of using spectral and radar backward scattering coefficients as feature parameters to enhance forest fire recognition accuracy for burnt areas. These findings suggest that appropriate machine learning algorithms should be employed under different conditions to obtain more precise identification of forest fire areas. This study provides technical support and empirical evidence for extracting and mapping forest fire areas while assessing damage caused by fires.

Existing studies often lack a systematic solution for Unmanned Aerial Vehicles (UAV) inspection system, which hinders their widespread application in crack detection. To enhance its practicality, this study proposes a formal and systematic framework for UAV inspection systems, specifically designed for automatic crack detection and pavement distress evaluation. The framework integrates UAV data acquisition, deep learning based crack identification, and road damage assessment in a comprehensive and orderly manner. Firstly, the flight control strategy is presented, and road crack data is collected using the DJI Mini 2 UAV imagery, establishing a high-quality UAV crack image datasets with ground truth information. Secondly, a validation and comparison study is conducted to enhance the automatic crack detection capability and provide an appropriate deployment scheme for UAV inspection systems. This study develops automatic crack detection models based on mainstream deep learning algorithms(namely, Faster-RCNN, YOLOv5s, YOLOv7-tiny, and YOLOv8s) in urban road scenarios. The results demonstrate that the Faster-RCNN algorithm achieves the highest accuracy and is suitable for online data collection of UAV and offline inspection at work stations. Meanwhile, the YOLO models, while slightly lower in accuracy, are the fastest algorithm and are suitable for lightweight deployment of UAV with online collection and real-time inspection. Quantitative measurement methods for road cracks are presented to assess road damage, which will enhance the application of UAV inspection systems and provide factual evidence for maintenance decisions made by road authorities.

In urban underground projects, such as urban drainage systems, the real-time acquisition and generation of 3D models of pipes can provide an important foundation for pipe safety inspection and maintenance. The simultaneous localization and mapping (SLAM) technique, compared to the traditional structure from motion (SfM) reconstruction technique, offers high real-time performance and improves the efficiency of 3D object reconstruction. Underground pipes are situated in complex environments with unattended individuals and often lack natural lighting. To address this, this paper presents a real-time and cost-effective 3D perception and reconstruction system that utilizes an unmanned aerial vehicle (UAV) equipped with Intel RealSense D435 depth cameras and an artificial light-supplementation device. This system carries out real-time 3D reconstruction of underground pipes using the RTAB-Map (real-time appearance-based mapping) method. RTAB-Map is a graph-based visual SLAM method that combines closed-loop detection and graph optimization algorithms. The unique memory management mechanism of RTAB-Map enables synchronous mapping for multiple sessions during UAV flight. Experimental results demonstrate that the proposed system, based on RTAB-Map, exhibits the robustness, textures, and feasibility for 3D reconstruction of underground pipes.