Elucidation of improved heuristic-assisted multi-dilated inception ResnetV2 with pyramidal attention for driver distraction detection

Globally, driver distractions are required to be recognised since it becomes a major cause for traffic-related fatalities. In recent years, the models limit with examining the specific feature information of the input source. Hence the main purpose of this paper is to develop an automated model of detecting the driver behaviour. Further, the collected images are subjected as input to Adaptive Multi-dilated Inception ResnetV2 with Pyramidal Attention (AMIR-PA) for classifying the distracted behaviours. In order to further enhance the performance, the hyper-parameters are optimally selected using Renovated Position-based Crocodile Optimisation Algorithm (RP-COA). Finally, the proposed system is validated using different measures and compared among traditional approaches. After the validation, the proposed model acquires high accuracy value as 91.45% and 92.2% for ReLU and tanh activation function than existing models. Therefore, the findings reveal that the proposed system achieves higher detection results to evade the traffic accidents that occur in roadways.