Multi-scale attentional feature fusion-based traffic sign detection using image dehazing process of SPP-CAE and improved optimization of YOLOv9 model.

For Intelligent Transportation Systems (ITS), traffic sign detection plays an essential role for autonomous vehicles to validate the condition of the roads more precisely. However, the environmental concerns, such as obstacles, lighting, and weather, may have an impact on the performance of the existing traffic sign detection systems. To overcome these problems, a model that uses a multi-scale-based learning network and image dehazing approach is developed in this work for traffic sign detection. At first, the required images are collected, and the input images undergo the pre-processing step. In this stage, the images utilize the Spatial Pyramid Pooling with Convolutional Autoencoder (SPP-CAE) for performing the image dehazing since the image is affected by unfavorable weather conditions. Finally, the pre-processed images are subjected as input to the object detection model, which employs the Multiscale Attentional Feature Fusion-based Feature Pyramid Network, incorporating Adaptive YOLOv9 (MAFF-FPN-AYv9). Here, the Multiscale Attentional Feature Fusion-based Feature Pyramid Network (MAFF-FPN) helped to extract the pertinent features and processed them under the YOLOv9 model for detecting the objects. For further improvement, the parameters present in the YOLOv9 model are optimally tuned by using the Stochastic Apiary Organizational Optimization with Population Amendment Strategy (SAOO-PAS). Lastly, the performance of the model is validated and examined by various metrics. With accuracies of 96% and 97% on the BDD100K and TT100K datasets, respectively, the results show how well this system performs, proving that the suggested model achieves superior results in detecting the traffic signs.