MOD-CL: Multi-label Object Detection with Constrained Loss

TL;DR

This paper presents MOD-CL, a multi-label object detection framework that employs constrained loss functions to improve output adherence to specified requirements, building upon YOLOv8 and introducing new post-processing models.

Contribution

The paper introduces MOD-CL with constrained loss integration and new post-processing models, enhancing multi-label object detection accuracy and output conformity.

Findings

Improved detection scores with constrained loss implementation.

Effective post-processing models for constrained outputs.

Enhanced multi-label detection performance.

Abstract

We introduce MOD-CL, a multi-label object detection framework that utilizes constrained loss in the training process to produce outputs that better satisfy the given requirements. In this paper, we use $\mathrm{MOD_{YOLO}}$, a multi-label object detection model built upon the state-of-the-art object detection model YOLOv8, which has been published in recent years. In 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 $\mathrm{MOD_{YOLO}}$ architecture using Product T-Norm. The results show that these implementations are instrumental to improving the scores for both Task 1 and Task 2.