Road object detection is a critical aspect of developing assistive technologies to enhance the mobility and safety of visually impaired individuals. In the context of Bangladesh, a densely populated and diverse environment, the need for accurate and contextually relevant road object detection is particularly significant. This paper introduces a novel road object detection dataset tailored specifically for the Bangladeshi context, aimed at facilitating the advancement of computer vision systems for assisting visually impaired pedestrians. The proposed dataset, named “BanROD” (Bangladesh Road Object Detection Dataset), is constructed by capturing images across Sylhet, Bangladesh. We primarily constructed this dataset which is trained on a baseline machine learning model. The dataset is annotated with meticulously labeled bounding boxes around road objects of interest including persons, CNGs, cars, upstairs-downstairs, and other obstacles. To ensure diversity, images are collected under different lighting conditions, weather scenarios, and traffic densities. In addition to dataset creation, we train and evaluate a deep learning-based object detection model “MobileNetV2” on the BanROD dataset. We then evaluate the model’s performance by measuring loss and average precision to find out how well it performs on real-world images. This solution is only for mobile devices as they are more handy for blind people around us.