A REVIEW ON ACCIDENT DETECTION USING DEEP LEARNING TO REDUCE THE RESPONSE TIME FOR MEDICAL HELP









Abstract

Every year, approximately 1.35 million people are cut off because of numerous crashes in the event of a road traffic accident. According to statistics, 20 to 50 million individuals are injured because of it. People lose their lives because of such road accidents. These situations are the result of a lack of coordination among the entities involved. In addition, failing to fully practice the rules and methods to be followed amplifies the graph upwards. Risk factors include speeding, drinking, and driving, distracted driving, poor infrastructure, unsafe vehicles, breaching rules, and many others. As a result, a system that is ideally capable of coordinating the many steps that must be conducted for a speedy response at the accident place is required. According to the research, such detection systems employ several technologies such as Deep Learning methodologies and machine learning approaches, among others. All vehicles are covered by these detection systems, and more technologies are being examined. This paper provides an overview of the technologies that are linked to road accidents via automated road [traffic] accident detection systems.


Modules


Algorithms


Software And Hardware