  The widespread adoption of Image Processing has propelled Object Recognition
(OR) models into essential roles across various applications, demonstrating the
power of AI and enabling crucial services. Among the applications, traffic sign
recognition stands out as a popular research topic, given its critical
significance in the development of autonomous vehicles. Despite their
significance, real-world challenges, such as alterations to traffic signs, can
negatively impact the performance of OR models. This study investigates the
influence of altered traffic signs on the accuracy and effectiveness of object
recognition, employing a publicly available dataset to introduce alterations in
shape, color, content, visibility, angles and background. Focusing on the
YOLOv7 (You Only Look Once) model, the study demonstrates a notable decline in
detection and classification accuracy when confronted with traffic signs in
unusual conditions including the altered traffic signs. Notably, the
alterations explored in this study are benign examples and do not involve
algorithms used for generating adversarial machine learning samples. 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.
