YOLO-SSFS: A Method Combining SPD-Conv/STDL/IM-FPN/SIoU for Outdoor Small Target Vehicle Detection

Abstract

As a vital part of autonomous driving, vehicle detection, especially for outdoor small target vehicles, has attracted great attention from researchers during recent years. To ameliorate the difficulty in accurately identifying outdoor small vehicle targets in dense environments, this paper proposes a new structure named YOLO-SSFS, in which SPD-Conv, a small target detection layer (STDL), the Improved Feature Pyramid Network structure (IM-FPN), and the SCYLLA-IoU (SIoU) loss function are introduced. Firstly, the multi-scale fusion module of the original algorithm is improved by adding a detection layer for smaller targets. This detection layer preserves shallow semantic information, which helps to refine the algorithm’s detection accuracy for small targets. Then, a new Convolutional Neural Network (CNN) building block named SPD-Conv is constructed to replace the pooling layers and convolutional layers in the YOLOv5 algorithm, which reduces information loss, ensures the original fine-grained details of the image and improves the learning ability. Afterwards, a new FPN structure is created to retain more information conducive to small target detection during the feature fusion process so as to enhance the robustness of the method. Finally, to speed up the convergence of the loss function, the SIoU loss function is introduced to replace Complete-IoU (CIoU) in the original algorithm. In order to verify the authenticity of the improved algorithm, we conduct a series of experiments on the VisDrone dataset and perform comparative analyses of the results. The results obtained demonstrate that compared with the original YOLOv5, the proposed model performs better in small target detection. The mean average precision (mAP) is 83.07%, which is 7.63% higher than that for YOLOv5, while the detection speed reaches 52 frames per second (FPS), meeting the requirements for real-time detection.