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Anomaly Detection on the Edge Using Smart Cameras under Low-Light Conditions.

Yaser Abu Awwad¹, Omer Rana¹, Charith Perera¹

¹Department of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UK.

Sensors (Basel, Switzerland)

|February 10, 2024

Summary

This summary is machine-generated.

This study introduces an image enhancement technique for low-light conditions, improving object detection accuracy in smart city surveillance. The method efficiently processes images on IoT-edge devices, reducing false positives and enhancing safety.

Keywords:

IoT-edge devices anomaly detection low-light image enhancement object detection

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Area of Science:

Computer Vision
Artificial Intelligence
Smart City Technology

Background:

Smart city surveillance systems increasingly use cameras in diverse environments.
Low-light conditions significantly challenge anomaly detection and object recognition.
Current methods struggle with accuracy and efficiency in poor visibility scenarios.

Purpose of the Study:

To develop an image enhancement technique for low-light conditions.
To improve object detection accuracy and reduce false positives in surveillance.
To enable efficient anomaly detection in challenging visual environments.

Main Methods:

Feature extraction from input images.
A classifier selects optimal multi-enhancement networks and distinguishes light conditions.
Object detection algorithm applied post-enhancement.
Implementation on separate IoT-edge devices for distributed processing.

Main Results:

Enhanced object detection accuracy in low-light environments.
Mitigation of false positive detections.
Nearly one-second response time across all processing stages on the ExDark database.
Improved overall detection performance.

Conclusions:

The proposed technique effectively enhances image quality in low-light conditions.
Distributed IoT-edge processing ensures efficient and rapid anomaly detection.
The research contributes to more reliable smart city surveillance and worker safety systems.