Vehicle Object Detection Algorithm Based on Region of Interest–Convolutional Neural Network

Traditional vehicle object detection faces problems such as low detection precision, high computational complexity, and poor performance in handling complex backgrounds. To address these challenges, this article adopts the simple linear iterative clustering (SLIC) algorithm for superpixel segmentation, generates candidate regions through selective search (SS), and uses the VGG16 deep convolutional neural network (CNN) for feature extraction, combined with a Softmax classifier for classification. Finally, the accuracy of vehicle detection boxes is improved by precisely adjusting the detection results through regional regression networks. In the training and testing of the model on large‐scale datasets, the combination of transfer learning and data augmentation techniques improves the model’s robustness and generalization capabilities. The experimental results show that the F1‐score of the model exceeds 0.95 in most vehicle categories, and the precision of the motorcycle detection reaches 0.978. The real‐time performance test shows that with high‐end graphics cards and optimization strategies, the model frame rate can reach 125 frames per second (FPS) and exhibits good robustness under complex lighting and weather conditions. Compared with the existing region of interest (ROI)–CNN‐based method, the SLIC superpixel + SS candidate region generation strategy proposed in this paper significantly reduces the missed detection of small vehicles and improves the quality of candidate frames by maintaining target boundary information at the superpixel level and performing multilevel merging, thereby improving the recall rate of small targets. At the same time, the VGG16 combined with dilated convolution feature extraction scheme effectively retains the contextual information in occluded scenes by expanding the receptive field without reducing the resolution of the feature map, thereby enhancing the recognition stability of partially occluded vehicles. This proves that the model based on the ROI–CNN is effective in improving detection accuracy and real‐time performance, showing its potential application value in applications such as intelligent transportation and autonomous driving.