Deepak Kumar Singh; Shyam Nandan Rai; K J Joseph, IIT Hyderabad; Rohit Saluja, Vineeth N Balasubramanian, IIT Hyderabad; Chetan Arora, IIT Delhi; Anbumani Subramanian, and C V Jawahar presented a poster on ORDER: Open World Object Detection on Road Scenes at NeurIPS 2021 – Workshop on Machine Learning for Autonomous Driving on 13 December. Research work as explained by the authors:
Object detection is a key component in autonomous navigation systems that enables localization and classification of the objects in a road scene. Existing object detection methods are trained and inferred on a fixed number of known classes present in road scenes. However, in real-world or open-world road scenes, while inference, we come across unknown objects that the detection model hasn’t seen while training. Hence, we propose Open World Object Detection on Road Scenes (ORDER) to address the aforementioned problem for road scenes. Firstly, we introduce Feature-Mix to improve the unknown object detection capabilities of an object detector. Feature-Mix widens the gap between known and unknown classes in latent feature space that helps improve the unknown object detection. Next, we identify that the road scene dataset compared to generic object dataset contains a significant proportion of small objects and has higher intra-class bounding box scale variations, making it challenging to detect the known and unknown objects. We propose a novel loss: Focal regression loss that collectively addresses the problem of small object detection and intra-class bounding box by penalizing more the small bounding boxes and dynamically changing the loss according to object size. Further, the detection of small objects is improved by curriculum learning. Finally, we present an extensive evaluation on two road scene datasets: BDD and IDD. Our experimental evaluations on BDD and IDD shows consistent improvement over the current state-of-the-art method. We believe that this work will lay the foundation for real-world object detection for road scenes.
Link to full paper:
Link to conference page: https://ml4ad.github.io/