SANet: Small but Accurate Detector for Aerial Flying Object

This paper proposes SANet, a small but accurate detector for aerial flying objects. The detector introduces an attention module into the feature extraction module (FEM) for enhancing the accuracy. This FEM with fewer convolutional kernel channels can reduce the parameters, speed up the inference time, and mitigate the computational burden. Furthermore, we optimize the Spatial Pyramid Pooling (SPP) module to enhance both the accuracy and speed. By analyzing the structure characteristic of the ResNet and RepVGG network that are usually utilized to extract features, a feature fusion module named RepNeck is designed to comprehensively fuse features extracted by the FEM, further enhancing the speed and accuracy. Eventually, we develop a neural network with an impressively small model size of only 4.5M. This network can achieve the state-of-the-art performance on three challenging datasets. Apart from its superior performance, our approach enjoys a real-time detection speed of 14.8 frames per second (fps) and power consumption of only 2.9W while the CPU and GPU temperatures are maintained below 50
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C even on an edge-computing device, highlighting the practicality of our approach for long-duration flying object detection and monitoring tasks.