Novel scorpion detection system combining computer vision and fluorescence

TL;DR

This paper presents a real-time, fully automatic scorpion detection system that combines computer vision and fluorescence detection under UV light, achieving high accuracy and 100% recall for safety applications.

Contribution

It introduces a novel dual-validation approach using shape features and fluorescence, integrating Haar Cascade and YOLO models for improved detection accuracy.

Findings

Achieved 100% recall in scorpion detection

Combined shape and fluorescence validation mechanisms

Demonstrated system effectiveness in dark environments

Abstract

In this work, a fully automatic and real-time system for the detection of scorpions was developed using computer vision and deep learning techniques. This system is based on the implementation of a double validation process using the shape features and the fluorescent characteristics of scorpions when exposed to ultraviolet (UV) light. The Haar Cascade Classifier (HCC) and YOLO (You Only Look Once) models have been used and compared as the first mechanism for the scorpion shape detection. The detection of the fluorescence emitted by the scorpions under UV light has been used as a second detection mechanism in order to increase the accuracy and precision of the system. The results obtained show that the system can accurately and reliably detect the presence of scorpions. In addition, values obtained of recall of 100% is essential with the purpose of providing a health security tool. Although the developed system can only be used at night or in dark environment, where the fluorescence emitted by the scorpions can be visualized, the nocturnal activity of scorpions justifies the incorporation of this second validation mechanism.