Light-YOLO: A lightweight and high-performance network for detecting small obstacles on roads at night

To address the challenges of detecting small obstacles and model portability, this study proposes a lightweight, high-precision, and high-speed small obstacle detection network at nighttime road environments referred to as Light-YOLO. First, the SPDConvMobileNetV3 feature extraction network is introduced, which significantly reduces the total number of parameters while enhancing the ability to capture small obstacle details. Next, to make the network more focused on small obstacles at nighttime conditions, a loss function called Wise-IoU is incorporated, which is more suitable to low-quality images. Finally, to improve overall model performance without increasing the total number of parameters, a parameter-free attention mechanism (SimAM) is integrated. By comparing the publicly available data with the self-built dataset, the experimental results show that Light-YOLO achieves a mean average precision ($mA{P}_{50}$) of 97.1% while maintaining a high image processing speed. Additionally, compared to other advanced models in the same series, Light-YOLO has fewer parameters, a smaller computational load (GFLOPs), and reduced model weight (Best.pt). Overall, Light-YOLO strikes a balance between lightweight design, accuracy, and speed, making it more suitable for hardware-constrained devices.