Digital Fencing – A Solution to Animal-Human Conflict

Fencing of animals is vital because of several factors such as providing safety and protection to them, containing them, preventing the spread of illness, and avoiding animal-human conflict. However, physical fencing of animals has various challenges such as being time-consuming, expensive, unmaintainable, and in some cases ineffective also. Therefore, in this research paper, we propose a digital fencing mechanism using a Deep Learning algorithm to tackle the abovementioned challenges. To be particular, we design a ready-to-use version of yoloV5, which aims at providing a reliable, secure, and easy-to-implement solution for the animal-human conflict. Moreover, we used an object detection technique for the classification of the animals that come in a certain region covered by the cameras. Thus, our approach does not affect animal rights as they are not being continuously tracked like the GPS and other methods of prevention unless they appear in a restricted area. Hence, this concept of digital fencing can do miraculous work in this field as it can help farmers protect their farms without investing too much effort and time and by also keeping the required safety measures for the animals as well as for themselves. To implement this work, we created our own dataset of 10,000 images belonging to seven different categories of animals. As a result, we achieved classification with a precision of 0.872, recall of 0.858, mAP@0.5 of 0.904, and mAP@0.5:0.95 of 0.584.