An Efficient Multiband Infrared Small Objects Detection Approach for Low-Altitude Artificial Intelligence of Things

As a cutting-edge technology of low-altitude Artificial Intelligence of Things (AIoT), autonomous aerial vehicle object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multiband infrared small object detection approach for low-altitude intelligent surveillance services. First, we propose the multiband infrared image fusion algorithm based on cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based multiscale dense small object detection (MsDSOD) algorithm is proposed. The algorithm consists of the global-local object detection (G-LOD) network, the object dense area extraction (O-DAE) module, and the weighted boxes fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean average precision (mAP) of MsDSOD is improved by 0.80% and the average precision in small object detection <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$({\mathrm { AP}}_{s})$ </tex-math></inline-formula> is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion.