Super Mamba feature enhancement framework for small object detection

It is very challenging to accurately and timely detect small object containing dozens of pixels from infrared images. Compared with the complex background in infrared images taken by low-altitude drones, a framework is designed to learn a strong feature representation separating the object from the background, which usually leads to a large computational amount. In this paper, we proposed a Super Mamba (SMamba) framework for UAV infrared small object detection, which performs deep learning of nonlinear complex data. Our SMamba framework performs high resolution object detection on multi-scale objects, considering both detection accuracy and computational cost. First, the Receptive Field Attention Convolution (RFAConv) is used into the backbone network and replaced the commonly convolution, and the multi-scale features is adjusted through the dynamic receptive field to optimize the computing efficiency. Furthermore, the Spatial Attention Mechanism (SAM) and Squeeze-Excitation (SE) are added to the State Space Model (SSM) to achieve multi-scale and multi-feature extraction for small object. Moreover, in the neck, the Feature Enhancement Module (FEM) is introduced to Bidirectional Feature Pyramid Network (BiFPN) can enhance the local context information of small objects and improve the detection efficiency. The experimental results show that Super Mamba achieved more than 92% accuracy on VEDAI dataset (in terms of mAP@ 0.5), which is more than 20% higher than the existing large models such as Yolov5, Yolov8, and Yolov11. The pytorch code is available at: https://github.com/wolfololo/Super-Mamba-A-Framework-for-Small-Object-Detection-with-Enhanced-Detection .