MACHINE LEARNING ALGORITHM FOR RAPID OBJECT DETECTION BASED ON COLOR FEATURES

Abstract

The identification of objects is of utmost importance in a wide range of computer vision applications, such as surveillance systems, autonomous cars, and environmental monitoring. Accurate and efficient object recognition methods are crucial in pastoral environments, characterized by the prominent presence of cattle and other objects, to provide effective analysis and decision-making processes. The present study paper introduces an innovative methodology for efficient identification of objects in pastoral landscapes through the utilization of a Colour Feature Extreme Learning Machine (CF-ELM). The CF-ELM method integrates color characteristics with the ELM algorithm to attain enhanced object detection accuracy while preserving computational economy. The experimental findings provide empirical evidence supporting the efficacy and efficiency of the suggested approach in the detection of items within pastoral landscapes. In addition to the CF-ELM, an algorithm for desktop-based categorization of items within a pastoral environment is provided, with individual speeds ranging from 0.05 s to 0.17 s for a single image, evaluated in each color space. The algorithm is intended for usage in scenarios with challenging and variable terrain, making it appropriate for application in agricultural or pastoral settings.