Methods for Ground Target Recognition from an Aerial Camera on a Helicopter Using the MISU-YOLOv8 Model in Dark and Foggy Environments

Abstract

Helicopters are critical aerial platforms, and their operational capability in complex environments is crucial. However, their performance in dark and foggy conditions is limited, particularly in ground target recognition using onboard cameras due to poor visibility and lighting conditions. To address this issue, we propose a YOLOv8-based model enhanced to improve ground target recognition in dark and foggy environments. The MS block is a multi-scale feature fusion module that enhances generalization by extracting features at different scales. The improved Residual Mobile Block (iRMB) incorporates attention mechanisms to enhance feature representation. SCINet, a spatial-channel attention-based network, adaptively adjusts feature map weights to improve robustness. UnfogNet, a defogging algorithm, enhances image clarity by removing fog. This integrated approach significantly improves ground target recognition capabilities. Unlike traditional models, AOD-Net generates clean images via a lightweight CNN, making it easily integrable into other deep models. Our MISU-YOLOv8 model outperforms recent state-of-the-art real-time object detectors, including YOLOv7 and YOLOv8, with fewer parameters and FLOPs, improving YOLOv8's Average Precision (AP) from 37% to over 41%. This work can also serve as a plug-and-play module for other YOLO models, this advancement provides robust technical support for helicopter reconnaissance missions in complex environments.