YOLOSCM: An improved YOLO algorithm for cars detection

TL;DR

YOLOSCM enhances vehicle detection in urban traffic images by introducing a segmentation clustering module and a new training strategy, effectively handling large images, small vehicles, and uneven vehicle distribution.

Contribution

The paper presents YOLOSCM, a novel framework combining segmentation clustering and optimized training for improved urban vehicle detection.

Findings

Improved detection accuracy in complex urban scenes

Effective handling of small and densely packed vehicles

Superior performance demonstrated on urban traffic datasets

Abstract

Detecting objects in urban traffic images presents considerable difficulties because of the following reasons: 1) These images are typically immense in size, encompassing millions or even hundreds of millions of pixels, yet computational resources are constrained. 2) The small size of vehicles in certain scenarios leads to insufficient information for accurate detection. 3) The uneven distribution of vehicles causes inefficient use of computational resources. To address these issues, we propose YOLOSCM (You Only Look Once with Segmentation Clustering Module), an efficient and effective framework. To address the challenges of large-scale images and the non-uniform distribution of vehicles, we propose a Segmentation Clustering Module (SCM). This module adaptively identifies clustered regions, enabling the model to focus on these areas for more precise detection. Additionally, we propose a new training strategy to optimize the detection of small vehicles and densely packed targets in complex urban traffic scenes. We perform extensive experiments on urban traffic datasets to demonstrate the effectiveness and superiority of our proposed approach.