An Investigation of Context-Aware Object Detection based on Scene Recognition

Visual scene understanding for humans entails identifying regions of interest and then reasoning about information gained through their contextual analysis. Attempts to reproduce this process using Computer Vision techniques play an important role in improving the performance of Object Detection and general scene understanding algorithms. In this paper, we augment the training algorithm of an Object Detection model by integrating contextual information in the form of ‘scene labels’, through various methods to identify the superior approach. Several deep learning models were implemented and evaluated; results show that the Contextually Aware Object Detection Model performed the best, producing the highest classification accuracy of 75.14% and mean bounding box IoU of 0.69 on the constructed dataset. This was achieved by virtue of an auxiliary scene classification model used to make image scene predictions during the training phase.