Abstract: Provided are a pedestrian detection method and system for autonomous driving under complex backgrounds. The detection method includes: acquiring a dataset including pedestrian images under different backgrounds and different degrees of occlusion; constructing an Automatic Driving You Only Look Once Respond for Pedestrian Background Influence Object Detection Network (AD-YOLO-RPBNet) model, where a backbone part includes four serial structures including convolution blocks and diverse branch C3K2 modules; in a neck part of the AD-YOLO-RPBNet model, one diverse branch C3K2 module is introduced in a first column, three alternating diverse branch C3K2 modules are introduced in a second column, and an output of each diverse branch C3K2 module in the second column is connected to a detection head via a diverse efficient local attention (DELA) module; and training the model by using the obtained dataset, where the trained model is used for pedestrian detection of autonomous driving under complex backgrounds.

Abstract: A Feature-adaptive multi-scale You Only Look Once (FamsYOLO) network-based method and system for detecting surface defects of new vehicles are provided. The method includes: acquiring images of front, rear, left, right, and top surfaces of different types of new vehicles before delivery, marking vehicle surface defects in the images, and constructing an image dataset; constructing a FamsYOLO network, where a backbone part of the FamsYOLO network is serially formed by a head DBL-3 module and an intermediate residual structure from top to bottom, and a neck part of the FamsYOLO network includes, from bottom to top in sequence, a DBL-1 module, upsampling, a first feature concatenation, a FasterC3K2 module, Reshape transformation, a second feature concatenation, a FasterC3K2 module, and a Squeeze-and-Excitation Network (SENet) module; and training the FamsYOLO network using the image dataset, where the trained FamsYOLO network is used for detecting surface defects of new vehicles.