An IoT based Monitoring System to Detect Animal in the Railway Track using Deep Learning Neural Network

Because of the increase in the cost of bus tickets, the railway has become one of the most popular methods of travel. A continual monitoring and examination of railroad tracks is essential in order for the railroad network to operate properly. Presently, the railroad track examination procedure and surveillance system are carried out by hand, which is both time-consuming and inefficient owing to the high likelihood of human mistake occurring throughout the process. Furthermore, since they travel huge distances, it is not practicable to examine and supervise the track on a continual basis using human power. Animal conservation is critical, and a great deal of technology has been used in a variety of methods to accomplish this. Because trains are really a usually applied means of transport in Asian nations, the railway system is even put down across forest regions, interfering with the activities of the local fauna. Larger animals are often struck by trains, and they die as a result. Such tragedies are widespread in India, particularly in the lush borders of the country's southern regions. In order to address the issue, this paper presents a computer skill approach that uses implantable recording devices to locate animals in close proximity to the source of the problem. The use of an inception model for such detection of animals in images and videos is suggested. The suggested approach has been tested and fine-tuned to recognize and identify animals. These very exact and precise models have the ability to alert the trains, maybe saving a valuable life.