Genetic brake-net: Deep learning based brake light detection for collision avoidance using genetic algorithm

Abstract

Automobiles are the primary means of transportation and increased traffic leads to the emphasis on techniques for safe transportation. Vehicle brake light detection is essential to avoid collisions among vehicles. Even though motorcycles are a common mode of transportation in many developing countries, little research has been done on motorcycle brake light detection. The effectiveness of Deep Neural Network (DNN) models has led to their adoption in different domains. The efficiency of the manually designed DNN architecture is dependent on the expert’s insight on optimality, which may not lead to an optimal model. Recently, Neural Architecture Search (NAS) has emerged as a method for automatically generating a task-specific backbone for object detection and classification tasks. In this work, we propose a genetic algorithm based NAS approach to construct a Mask R-CNN based object detection model. We designed the search space to include the architecture of the backbone in Mask R-CNN along with attributes used in training the object detection model. Genetic algorithm is used to explore the search space to find the optimal backbone architecture and training attributes. We achieved a mean accuracy of 97.14% and 89.44% for detecting brake light status for two-wheelers (on NITW-MBS dataset) and four-wheelers (on CaltechGraz dataset) respectively. The experimental study suggests that the architecture obtained using the proposed approach exhibits superior performance compared to existing models.