At this moment, many special vehicles are engaged in illegal activities such as illegal mining, oil and gas theft, the destruction of green spaces, and illegal construction, which have serious negative impacts on the environment and the economy. The illegal activities of these special vehicles are becoming more and more rampant because of the limited number of inspectors and the high cost required for surveillance. The development of drone remote sensing is playing an important role in allowing efficient and intelligent monitoring of special vehicles. Due to limited onboard computing resources, special vehicle object detection still faces challenges in practical applications. In order to achieve the balance between detection accuracy and computational cost, we propose a novel algorithm named YOLO-GNS for special vehicle detection from the UAV perspective. Firstly, the Single Stage Headless (SSH) context structure is introduced to improve the feature extraction and facilitate the detection of small or obscured objects. Meanwhile, the computational cost of the algorithm is reduced in view of GhostNet by replacing the complex convolution with a linear transform by simple operation. To illustrate the performance of the algorithm, thousands of images are dedicated to sculpting in a variety of scenes and weather, each with a UAV view of special vehicles. Quantitative and comparative experiments have also been performed. Compared to other derivatives, the algorithm shows a 4.4% increase in average detection accuracy and a 1.6 increase in detection frame rate. These improvements are considered to be useful for UAV applications, especially for special vehicle detection in a variety of scenarios.

In recent years, point cloud super-resolution technology has emerged as a solution to generate a denser set of points from sparse and low-quality point clouds. Traditional point cloud super-resolution methods are often optimized based on constraints such as local surface smoothing; thus, these methods are difficult to be used for complex structures. To address this problem, we proposed a novel graph convolutional point cloud super-resolution network based on a mixed attention mechanism (GCN-MA). This network consisted of two main parts, i.e., feature extraction and point upsampling. For feature extraction, we designed an improved dense connection module that integrated an attention mechanism and graph convolution, enabling the network to make good use of both global and local features of the point cloud for the super-resolution task. For point upsampling, we adopted channel attention to suppress low-frequency information that had little impact on the up-sampling results. The experimental results demonstrated that the proposed method significantly improved the point cloud super-resolution performance of the network compared to other corresponding methods.