Enhanced Detection of Small Objects in Aerial Imagery: A High-Resolution Neural Network Approach With Amplified Feature Pyramid and Sigmoid Re-Weighting

Detecting small objects in drone-captured images or aerial videos is challenging due to their minimal representation. As data traverses deep learning networks, the information about small objects can diminish, making high-resolution images essential for enhanced detection performance. However, high-resolution images increase computational load undesirably. Leveraging this fact, we propose a streamlined neural network designed specifically for small object detection in high-resolution images. The proposed network encompasses three main components: i) Enhanced High-Resolution Processing Module (EHRPM), ii) the Small Object Feature Amplified Feature Pyramid Network (SOFA-FPN) with its Edge Enhancement Module (EEM), Cross Lateral Connection Module (CLCM), and Dual Bottom-up Convolution Module (DBCM), and iii) the Sigmoid Re-weighting Module (SRM). Compared to several state-of-the-art networks, our method delivers superior performance with fewer parameters and a lower computational demand. The source code is available at https://github.com/datu0615/EHRPM.