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Drones play an important role in the development of remote sensing and intelligent surveillance. Due to limited onboard computational resources, drone-based object detection still faces challenges in actual applications. By studying the balance between detection accuracy and computational cost, we propose a novel object detection algorithm for drone cruising in large-scale maritime scenarios. Transformer is introduced to enhance the feature extraction part and is beneficial to small or occluded object detection. Meanwhile, the computational cost of the algorithm is reduced by replacing the convolution operations with simpler linear transformations. To illustrate the performance of the algorithm, a specialized dataset composed of thousands of images collected by drones in maritime scenarios is given, and quantitative and comparative experiments are conducted. By comparison with other derivatives, the detection precision of the algorithm is increased by 1.4%, the recall is increased by 2.6% and the average precision is increased by 1.9%, while the parameters and floating-point operations are reduced by 11.6% and 7.3%, respectively. These improvements are thought to contribute to the application of drones in maritime and other remote sensing fields.
Li et al. (Mon,) studied this question.