LaB-CL: Localized and Balanced Contrastive Learning for improving parking slot detection

TL;DR

This paper introduces LaB-CL, a novel supervised contrastive learning framework that addresses data imbalance in parking slot detection, significantly improving classification accuracy in autonomous parking systems.

Contribution

LaB-CL is the first contrastive learning approach specifically designed for parking slot detection, incorporating class prototypes and hard negative sampling to enhance performance.

Findings

Outperforms existing parking slot detection methods on benchmark datasets.

Effectively mitigates data imbalance issues in parking slot classification.

Demonstrates improved detection accuracy through contrastive learning techniques.

Abstract

Parking slot detection is an essential technology in autonomous parking systems. In general, the classification problem of parking slot detection consists of two tasks, a task determining whether localized candidates are junctions of parking slots or not, and the other that identifies a shape of detected junctions. Both classification tasks can easily face biased learning toward the majority class, degrading classification performances. Yet, the data imbalance issue has been overlooked in parking slot detection. We propose the first supervised contrastive learning framework for parking slot detection, Localized and Balanced Contrastive Learning for improving parking slot detection (LaB-CL). The proposed LaB-CL framework uses two main approaches. First, we propose to include class prototypes to consider representations from all classes in every mini batch, from the local perspective. Second, we propose a new hard negative sampling scheme that selects local representations with high prediction error. Experiments with the benchmark dataset demonstrate that the proposed LaB-CL framework can outperform existing parking slot detection methods.