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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.

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