As a rule, tunnels are considered safe road infrastructures. Nevertheless, when an accident occurs inside a tunnel it can maximize its impact and casualties due to its constrained space of occurring events. Undoubtedly, fire accident events are the greatest threat to road tunnel systems and destructive experiences such as the Mont Blanc fire in France (1999) or the fire in Yanhou, China (2014) are indicative of the severity of such incidents. The use of automated deep learning and data mining algorithms that can provide accurate detection, frequency patterns and concentration predictions of dangerous goods passing through tunnels, is a significant fire incident restriction factor. To achieve automated detection, a post processing image detection tool has been developed, that identifies and marks the passage of dangerous goods through tunnels. This tool receives input from toll camera images and offers timely information of vehicles carrying dangerous goods, since such vehicles are signalled with a proper ADR label number (ADR vehicles). Knowing the exact number of ADR vehicles along with their carrying substance at any particular time, followed by classification and associated rules to fire incident occurrences, can lead to an effective management of the passage of such vehicles and consequently to an effective preventive management of fire incidents in tunnels.