Aiming at the problem of lane detection accuracy of intelligent vehicles in complex scenes, this paper proposed a lane line detection algorithm which incorporates a multi-scale spatial attention mechanism and a path aggregation network (PANet). The algorithm first introduced the pre-anchored frame UFLD lane detection model and incorporated a feature pyramid enhancement module PANet with depthwise separable convolution to achieve multi-scale feature extraction of images. Next, a multi-scale spatial attention module was designed in the network framework and a SimAM lightweight attention mechanism was introduced to enhance the focusing ability on target features. Then, an adaptive feature fusion module was designed to perform cross-scale fusion of feature maps output from PANet by intelligently adjusting the fusion weights of feature maps at different scales, so as to effectively enhance the network’s ability to extract complex features. Finally, the application of TuSimple dataset detection proves that the proposed algorithm achieves a detection accuracy of 96.84%, representing a 1.02 percentage point improvement over the original algorithm, and outperforms conventional mainstream algorithms. Experimental results on the CULane dataset demonstrate that the proposed algorithm achieves an F₁ score of 72.74%, outperforming conventional mainstream methods with a 4.34 percentage point improvement over the baseline. Notably, it exhibits significant performance gains in extreme scenarios (e.g., strong illumination and shadows), confirming its superior detection capability in complex environments. In addition, the real-time test shows that the model inference speed reaches 118 f/s, which meets the real-time demand of intelligent vehicles.

Aiming at the problem that the current object detection algorithm has many parameters and larger computation amount, resulting in slower response and difficulties of application in intelligent vehicle systems, this paper proposed an improved CenterNet object detection algorithm. That is, the lightweight MobileNetV3 network was applied to replace the original ResNet-50 network to reduce the amount of computation; the depth-separable PANet was applied to replace the feature enhancement network to obtain the features after the fusion of the multi-scale feature information, and the SimAM attention mechanism was introduced to strengthen the attention of the target features before the fusion of the features, then the SiLU activation function was used to replace the original object detection network in the ReLU activation function to enhance the network learning. Finally, the CPAN-ASFF module was proposed to fuse the depth-separable PANet output multi-scale feature maps to improve the object detection accuracy. The optimized KITTI dataset was applied to train and detect the improved CenterNet object detection algorithm for validation. And the results show that its mean average precision is 80.7%, which is 12 percentage points higher than that of the original CenterNet object detection algorithm; its detection speed is 65 f/s, and its number of parameters is 8.91 M, which is 72.73% less than the original algorithm. The improved algorithm performs better in the detection of occluded objects, overlapped objects and objects similar to the background. In the SODA10M dataset, the detection accuracy and speed of the algorithm are better than the current mainstream algorithms. The optimization of the algorithm and the experiments in the paper laid the technical support for the application of intelligent vehicles in practical engineering.

In response to the issue of reduced road environment perception accuracy for intelligent vehicles under extreme haze conditions, this paper proposed a joint haze environment perception algorithm based on an improved CycleGAN and YOLOv8. Firstly, the CycleGAN algorithm was used as the framework for image defogging preprocessing. A self-attention mechanism was incorporated into the generator network to enhance the network’s feature extraction capability. Additionally, to minimize color discrepancies with real images, a self-regularized color loss function was introduced. Secondly, in the object detection phase, the lightweight GhostConv network was first used to replace the original backbone network, reducing computational complexity. Furthermore, the GAM attention mechanism was added to the neck network to effectively improve the network’s ability to interact with global information. Finally, the WIoU loss function was used to mitigate harmful gradients caused by low-quality samples, improving the model’s convergence speed. Experiments conducted on the RESIDE and BDD100k datasets validate the proposed algorithm. Results show that the structural similarity between dehazed and original images is 85%. Compared to the original CycleGAN algorithm and the AODNet algorithm, the proposed approach improves the peak signal-to-noise ratio (PSNR) by 2.24 dB and 2.5 dB, respectively, and the structural similarity index (SSIM) by 15.4% and 36.3%, respectively. Additionally, the improved YOLOv8 algorithm demonstrates enhancements over the original algorithm, with precision, recall, and mean average precision (mAP) increasing by 2.5%, 1.8%, and 1.1%, respectively. The experimental results confirm that the proposed algorithm outperforms traditional algorithms in terms of recall and detection accuracy, demonstrating its practical value