E2D2: Elephant Emotion and Distraction Detection Framework using Edge-Enabled YOLOv5 Deep Learning Algorithm

Emotions not only remained as an instrument of choice to unite humans but also to establish human-animal bonds. Identifying emotions from two distinct binaries among the livelihood animals, the home and wild livelihood animals, mandated specialized knowledge or skilled mahouts in the past. This article proposed an Elephant Emotion and Distraction Detection (E2D2) framework using YOLOv5-based deep learning algorithms to detect the emotions of elephants such as fear, anger, joy, and neutrality, in two different approaches - Elephant Emotion Detection (EED) approach and Elephant Emotional Feature Detection (EEFD) approach. E2D2 captures the distracting moments of elephants while working on gargantuan tasks such as carrying wooden logs. The article manifests the importance of the proposed E2D2 framework when experimenting on edge-enabled heterogeneous devices, including Jetson Nano. Our approach achieved an accuracy of 83.28% to 88.89% when experimented with elephant videos in real time. The framework can benefit people while approaching elephants; it can prevent tourists from elephant attacks; and, it can enhance the working abilities of trained elephant-based logistics by establishing strong social bonds with mahouts.

I. Introduction

Animals occupy a key strategic position that influences the working potentials in modern era. A few animals upsurge the productivity among software/business professionals [6]. The increasing animal-inclusive culture at work, recently, has intensified the interest of AI researchers to study human-animal bonds using sophisticated technologies such as IoT, cloud, and edge computing [18].