EFFICIENT OBJECT DETECTION IN AGRICULTURAL ENVIRONMENTS IMPLEMENTING COLOR FEATURES EXTREME LEARNING MACHINE

In a wide variety of computer vision applications, such as surveillance systems, autonomous cars, and environmental monitoring, object detection is an extremely important component. For the purpose of conducting effective analysis and making sound decisions, it is vital to have object identification methods that are both accurate and efficient in pastoral environments, which are characterized by the presence of animals and other things. The purpose of this research study is to present a unique method for the rapid recognition of objects in pastoral landscapes by utilizing a Color Feature Extreme Learning Machine (CF-ELM). For the purpose of achieving higher object detection performance while maintaining computational efficiency, the CF-ELM integrates color characteristics with the ELM algorithm. The proposed method is shown to be successful and efficient in detecting objects in pastoral environments, as demonstrated by the results of the experiments.