[2M1-OS-11a] OS-11

[2M1-OS-11a-03] Introducing Constraints to Multilabel Object Detection and application to ROAD-R

〇Sota Moriyama^1,2, Koji Watanabe^1,3, Katsumi Inoue^1,2,3, Akihiro Takemura¹ (1. National Institute of Technology, 2. Tokyo Institute of Technology, 3. The Graduate University for Advanced Studies)

Keywords:Object Recognition, Boolean Satisfiability Problem, Constrains, Autonomous Driving

Detecting the actions of each object is detrimental to improving the usability of the model, but the risk of misrecognition increases as the number of label combinations increases. Therefore, we propose a framework that reduces the amount of misrecognition by utilizing the requirements that the set of labels has to satisfy. Specifically, we propose MOD_YOLO, a novel multilabel object detection model built upon the state-of-the-art object detection model YOLOv8, and develop our framework on top of it. We then assess the framework's effectiveness by applying it to the ROAD-R Challenge for NeurIPS 2023 competition. For Task 1, we introduce the Corrector Model and Blender Model, two new models that follow after the object detection process, aiming to generate a more constrained output. For Task 2, constrained losses have been incorporated into the training process of MOD_YOLO using Fuzzy Logic. The results show that using the above framework was instrumental to improving the scores for both Tasks 1 and 2, allowing us to place third and first in the subsequent tasks.