Effects of Real-Life Traffic Sign Alteration on YOLOv7- an Object Recognition Model

The advancement of Image Processing has led to the widespread use of Object Recognition (OR) models in various applications, such as airport security and mail sorting. These models have become essential in signifying the capabilities of AI and supporting vital services like national postal operations. However, the performance of OR models can be impeded by real-life scenarios, such as traffic sign alteration. Therefore, this research investigates the effects of altered traffic signs on the accuracy and performance of object recognition models. To this end, a publicly available dataset was used to create different types of traffic sign alterations, including changes to size, shape, color, visibility, and angles. The impact of these alterations on the YOLOv7 (You Only Look Once) model's detection and classification abilities were analyzed. It reveals that the accuracy of object detection models decreases significantly when exposed to modified traffic signs under unlikely conditions. This study highlights the significance of enhancing the robustness of object detection models in real-life scenarios and the need for further investigation in this area to improve their accuracy and reliability.