Attention mechanism and point cloud object detection model for intelligent driving object detection technology

Object detection technology is a key technology in intelligent driving vehicles, which has a significant impact on vehicle driving safety. With the popularization of intelligent driving vehicles, people's attention to object detection technology is constantly increasing. At present, many object detection technologies have the problem of large object detection errors. To solve this problem, this study optimized the iterative nearest point cloud registration algorithm using convolutional neural networks and convolutional attention modules, and proposed an object detection model based on the optimized algorithm. The study applied optimization algorithms for detection in different types of datasets, and the outcomes revealed that the feature matching error of the optimization algorithm was less than 3% in all datasets. Further analysis of the constructed object detection model showed that its accuracy rate was as high as 97.6%, with a detection time of only 0.67 seconds. Moreover, the model achieved an accuracy rate of over 97% in tunnel, occlusion, rain, snow, and haze weather, demonstrating strong anti-interference capabilities. From the above results, the proposed object detection model could improve the accuracy of object detection in intelligent driving vehicles, thereby providing a guarantee for the safe driving of intelligent driving vehicles.