Xinbao Chen

Intelligent bird species recognition is vital for biodiversity monitoring and ecological conservation. This study tackles the challenge of declining recognition accuracy caused by occlusions and imaging noise in complex natural environments. Focusing on ten representative bird species from the Dongting Lake Wetland, we propose an improved YOLOv11n-based model named MSFN-YOLO11, which incorporates multi-scale feature fusion. After selecting YOLOv11n as the baseline through comparison with the most-stable version of YOLOv8n, we enhance its backbone by introducing an MSFN module. This module strengthens global and local feature extraction via parallel dilated convolution and a channel attention mechanism. Experiments are conducted on a self-built dataset containing 4540 images of ten species with 6824 samples. To simulate real-world conditions, 25% of samples are augmented using random occlusion, Gaussian noise (σ = 0.2, 0.3, 0.4), and Poisson noise. The improved model achieves a mAP@50 of 96.4% and mAP@50-95 of 83.2% on the test set. Although the mAP@50 shows a slight improvement of 0.3% compared to the original YOLOv11, it has contributed to an 18% reduction in training time. Furthermore, it also demonstrates practical efficacy in processing dynamic video, attaining an average 63.1% accuracy at 1920 × 1080@72fps on an NVIDIA_Tesla_V100_SXM2_32_GB. The proposed model provides robust technical support for real-time bird monitoring in wetlands and enhances conservation efforts for endangered species.

Road cracks represent a significant challenge that impacts the long-term performance and safety of transportation infrastructure. Early identification of these cracks is crucial for effective road maintenance management. However, traditional crack recognition methods that rely on visible light images often experience substantial performance degradation in weak-light environments, such as at night or within tunnels. This degradation is characterized by blurred or deficient image textures, indistinct target edges, and reduced detection accuracy, which hinders the ability to achieve reliable all-weather target detection. To address these challenges, this study introduces a dual-modal crack detection method named YOLOv11-DCFNet. This method is based on an enhanced YOLOv11 architecture and incorporates a Cross-Modality Fusion Transformer (CFT) module. It establishes a dual-branch feature extraction structure that utilizes both infrared and visible light within the original YOLOv11 framework, effectively leveraging the high contrast capabilities of thermal infrared images to detect cracks under weak- or no-light conditions. The experimental results demonstrate that the proposed YOLOv11-DCFNet method significantly outperforms the single-modal model (YOLOv11-RGB) in both weak-light and no-light scenarios. Under weak-light conditions, the fusion model effectively utilizes the weak texture features of RGB images alongside the thermal radiation information from infrared (IR) images. This leads to an improvement in Precision from 83.8% to 95.3%, Recall from 81.5% to 90.5%, mAP@0.5 from 84.9% to 92.9%, and mAP@0.5:0.95 from 41.7% to 56.3%, thereby enhancing both detection accuracy and quality. In no-light conditions, the RGB single modality performs poorly due to the absence of visible light information, with an mAP@0.5 of only 67.5%. However, by incorporating IR thermal radiation features, the fusion model enhances Precision, Recall, and mAP@0.5 to 95.3%, 90.5%, and 92.9%, respectively, maintaining high detection accuracy and stability even in extreme no-light environments. The results of this study indicate that YOLOv11-DCFNet exhibits strong robustness and generalization ability across various low illumination conditions, providing effective technical support for night-time road maintenance and crack monitoring systems.

Rapid urbanization and climate change have exacerbated urban heat stress, underscoring the importance of research on human thermal comfort for sustainable urban development. This study analyzes the spatiotemporal variation and driving factors of apparent temperature in the Chang–Zhu–Tan urban agglomeration, China. The Humidex index, representing apparent temperature, was derived from multi-source remote sensing data (Landsat 8, MODIS) and meteorological variables (ERA5-Land reanalysis), employing atmospheric correction, random forest modeling, and path analysis. The results indicate pronounced spatiotemporal heterogeneity: apparent temperature reached its maximum in urban centers during summer (mean 52.9 °C) and its minimum in winter (mean 5.99 °C), following a decreasing gradient from urban core to periphery. Land cover emerged as a key driver, with vegetation (NDVI, r = −0.938) showing a strong negative correlation and built-up areas (NDBI, r = +0.8) a positive correlation with apparent temperature. Uniquely, in the Chang–Zhu–Tan region’s persistently high humidity, water bodies (MNDWI, r = +0.616) exhibited a positive correlation with apparent temperature, likely due to humidity-enhanced thermal perception in summer and relatively warmer water temperature in winter. Path analysis revealed that air temperature exerts the strongest direct positive influence on apparent temperature, while relative humidity and NDVI primarily act through indirect pathways. These findings provide scientific evidence to guide climate-adaptive urban planning and enhance human living conditions in humid environments.

Accurately obtaining the 3D geometry and texture of urban pipeline interiors is crucial for pipeline inspection, urban planning, and digital management. The interior environment of urban pipelines is characterized by insufficient illumination and a lack of textural features, which poses significant challenges for real-time 3D reconstruction. In this study, we propose a novel multi-sensor fusion system based on the enhanced R³LIVE algorithm, which integrates LiDAR, cameras, and Inertial Measurement Unit(IMU). Our approach addresses the performance degradation of LiDAR and vision systems within pipelines by employing a hybrid optical-flow method based on the Convolutional Neural Networks(CNN), which significantly enhances the accuracy and efficiency of tracking feature points on the inner wall of pipeline. Quantitative experimental results obtained in a real urban pipeline environment demonstrate that the accuracy and robustness of the 3D reconstruction are substantially improved, achieving a relative rotation error (RRE) of 1.75 ° and a relative translation error (RTE) of 2.41% over a range of 150 m. Moreover, the algorithm significantly outperforms the FAST-LIVO, FAST-LIO2, and the original R³LIVE conventional simultaneous localization and mapping (SLAM) methods at longer ranges. These results underscore the practical value and superiority of our multi-sensor fusion method for the 3D reconstruction of urban pipelines.

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.

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.

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 various sensors are expensive and not suitable for mountainous roads due to poor conditions. To address these challenges, this study proposes a customized Unmanned Aerial Vehicles (UAV) inspection system designed for automatic crack detection. The 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 is proposed to enhance the autonomous navigation of UAV flights by generating path planning in mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the YOLOv8 algorithm is introduced to conduct autonomous crack detection from UAV imagery in the offboard module. To validate our UAV inspection system's performance, we conducted several experiments to assess its accuracy, robustness, and efficiency. The experimental results demonstrate that our UAS inspection system accurately estimates the flight trajectory along the mountainous road for autonomous navigation. Furthermore, the experimental detection results also demonstrate that the YOLOv8 algorithm achieves higher recognition accuracy compared to the YOLOv5, and YOLOv7-tiny algorithms, exceeding 3.1%,and 6.8% of mAP, respectively. In summary, the UAV inspection system proposed in this study can provide a useful tool and technological guidance for the regular inspection of mountainous roads.