Training A Neural Network For Partially Occluded Road Sign Identification In The Context Of Autonomous Vehicles

Abstract

The increasing number of autonomous vehicles and the rapid development of computer vision technologies underscore the particular importance of conducting research on the accuracy of traffic sign recognition. Numerous studies in this field have already achieved significant results, demonstrating high effectiveness in addressing traffic sign recognition tasks. However, the task becomes considerably more complex when a sign is partially obscured by surrounding objects, such as tree branches, billboards, or other elements of the urban environment. In our study, we investigated how partial occlusion of traffic signs affects their recognition. For this purpose, we collected a dataset comprising 5,746 images, including both fully visible and partially occluded signs, and made it publicly available. Using this dataset, we compared the performance of our custom convolutional neural network (CNN), which achieved 96% accuracy, with models trained using transfer learning. The best result was obtained by VGG16 with full layer unfreezing, reaching 99% accuracy. Additional experiments revealed that models trained solely on fully visible signs lose effectiveness when recognizing occluded signs. This highlights the critical importance of incorporating real-world data with partial occlusion into training sets to ensure robust model performance in complex practical scenarios and to enhance the safety of autonomous driving.