Improving Object Detection in Real-World Traffic Scenes

Abstract

Single Shot Multi-Box Detector (SSD) is a well-known object detection algorithm. It can detect 20 different types of objects making it suitable for an object detector for traffic scenes. In a real-world traffic scene, objects can appear in different sizes and pose different details. This can potentially lead to false detections made by an SSD. Depending on how input information (image) is provided to SSD (leading to a proposed SSD model), the accuracy of the proposed model can vary. The overall objective of this study is to evaluate different SSD models while examining accuracy of object detection where the object type is only a vehicle. This study is derived from human vision. Where, an object is easily identifiable in a sharper image with brightness than a blurry one with darkness. Based on these assumptions hypotheses were created, based on which SSD based models were proposed. Comparison based on true positives and false positives was performed and the winner was identified by using the Enpeda. Image Sequence Analysis Test Site (EISATS) stereo image barriers dataset set 9.