The organizational management of construction sites is a critical aspect in engineering management; however, traditional human supervision method is constrained by many environment limitations and low efficiency. In recent years, multiple government departments have issued relevant policies advocating deep integration of artificial intelligence with the real economy to promote high-quality and efficient economic development. The accuracy, efficiency, and automation advantages of Computer Vision (CV) technology have gradually led to its widespread application in the field of construction supervision. Meanwhile, the drones, which can efficiently obtain complex and varied visual data of construction scene, demonstrate their application potential in CV-based construction supervision tasks. However, the current researches on drone-based construction scene detection are limited, and the lack of overhead-perspective construction-scene image datasets restricts further development in the field. Therefore, the DJI Mavic 3T drone was utilized to obtain construction-site images to establish an open-source overhead image dataset for construction scene UB-CSD. Several advanced object-detection algorithms were selected for comparative experiments on the UB-CSD dataset, and the reasons for performance differences were analyzed from multiple dimensions such as model workflow design, computation principle, and task characteristics. The mAPs of every algorithm’s detection result were YOLOv8 and YOLOv10 (96.1%), YOLOv9 (96.0%), YOLO11 (95.7%), DETR (95.3%), Faster-RCNN (76.3%) and RetinaNet (72.1%). The analysis results indicated that the YOLO series algorithm constituted the most optical algorithm for drone-based object detection tasks in construction scenes. By establishing a new open-source special dataset and conducting comparative experiments, the conclusion drawn provided effective data and experimental cases to support future safety production management and object-detection algorithm research in the construction industry.

To address the challenge of achieving high-quality 3D reconstruction with Neural Radiation Fields (NeRF) under the influence of occluding objects, a method based on the collaborative optimization of Structure-from-Motion (SfM) and the Segment Anything Model (SAM) was propose. Building upon the Scale-Invariant Feature Transform (SIFT) algorithm within the SfM reconstruction process, geometric inconsistencies in dynamic scenes were leveraged for feature point identification and matching. Unmatched feature points were treated as dynamic occluders, guiding the SAM model—capable of point-guided segmentation—to perform dynamic occluder segmentation and generate a static scene mask. Based on the segmentation results, mask-aware volumetric rendering was used to predict colors and a quadruple loss function was established: comprising reconstruction loss, structural consistency loss, adversarial loss, and self-supervised patching loss. These objectives were jointly optimized to constrain the color output in patched regions. After iterative training, consistent restoration of geometric structure and appearance in occluded areas across multiple viewpoints was achieved. The radiometric integrity was preserved while occlusions were removed. Validation on public dynamic scene datasets demonstrated that the mask-based volumetric rendering combined with joint optimization produced an average Peak Signal-to-Noise Ratio (PSNR) improvement of 5.24 dB over baseline models and mainstream occlusion removal methods, alongside a 35% reduction in Learned Perceptual Image Patch Similarity (LPIPS). This approach established a new paradigm for 3D reconstruction in complex dynamic environments.