Real-Time Detection of Road-Based Objects using SSD MobileNet-v2 FPNlite with a new Benchmark Dataset

Abstract

This research paper presents a real-time detection of road-based objects using SSD MobileNet-v2 FPNlite. This model uses the Single Shot Detector (SSD) architecture with MobileNet-v2 as the backbone and Feature Pyramid Network lite (FPNlite) as the feature extractor. This approach combines the advantages of both SSD and MobileNet-v2 for object detection while maintaining low computational complexity. In order to evaluate the performance of the model, a new benchmark dataset is explicitly created for this study, which includes a wide range of images captured from various sources such as cameras mounted on vehicles and street-level cameras. The dataset contains a diverse set of objects and scenes, making it suitable for testing the robustness and generalization ability of the system. The results of the experiments demonstrate the effectiveness of the model. In addition, the newly developed benchmark dataset can be used as a reference for further research in the field.