MSF-GhostNet: Computationally Efficient YOLO for Detecting Drones in Low-Light Conditions

Uncrewed aerial vehicles (UAVs) are popular in various applications due to their mobility, size, and user-friendliness. However, identifying malicious UAVs presents challenges that need to be encountered in general image-based object detection. These challenges arise because UAVs can fly at different altitudes, making it challenging to distinguish them from other flying objects and identify their size. In addition, the speed of UAVs also adds to the difficulty of capturing their clear images, which can lead to blurring, particularly in complex backgrounds. To address these challenges, we present an improved YOLOv5 architecture named multiscale feature map GhostNet (MSF-GhostNet) by introducing GhostConv and C3Ghost modules to reduce the redundant operations in the head and neck. We also proposed three feature map combinations to evaluate the performance of multiscale and multitarget flying objects, including drones, birds, planes, and helicopters. This approach significantly reduces the waste of computing resources when detecting small-sized flying objects. We also integrated autoanchor and batch size mechanisms to ensure efficient model training and avoid overfitting. Our proposed model showed 1.25% fewer false positives than the state-of-the-art GhostNet-YOLOv5 model. The proposed MSF-GhostNet outperformed GhostNet-YOLOv5 with higher precision, recall, and F1 scores (1.3%, 5.3%, and 3.7%, respectively) and reduced model parameters and model size by 3.1% and 4.1%, respectively. The proposed solution also outperformed several other state-of-the-art algorithms exists in the literature.