Atmospheric Noise-Resilient Image Classification in a Real-World Scenario: Using Hybrid CNN and Pin-GTSVM

TL;DR

This paper introduces a hybrid CNN and Pin-GTSVM model for parking space detection that maintains high accuracy under atmospheric haze, eliminating the need for dehazing and improving real-world robustness.

Contribution

A novel hybrid model combining pre-trained feature extraction with Pin-GTSVM for atmospheric noise-resilient parking space classification, removing the need for dehazing.

Findings

Significant accuracy improvement in hazy conditions

Effective integration with existing parking infrastructure

Outperforms existing methods on multiple datasets

Abstract

Parking space occupation detection using deep learning frameworks has seen significant advancements over the past few years. While these approaches effectively detect partial obstructions and adapt to varying lighting conditions, their performance significantly diminishes when haze is present. This paper proposes a novel hybrid model with a pre-trained feature extractor and a Pinball Generalized Twin Support Vector Machine (Pin-GTSVM) classifier, which removes the need for a dehazing system from the current State-of-The-Art hazy parking slot classification systems and is also insensitive to any atmospheric noise. The proposed system can seamlessly integrate with conventional smart parking infrastructures, leveraging a minimal number of cameras to monitor and manage hundreds of parking spaces efficiently. Its effectiveness has been evaluated against established parking space detection methods using the CNRPark Patches, PKLot, and a custom dataset specific to hazy parking scenarios. Furthermore, empirical results indicate a significant improvement in accuracy on a hazy parking system, thus emphasizing efficient atmospheric noise handling.