Automatic vehicle detection system in different environment conditions using fast R-CNN

Vehicle detection and classification is a challenging move in the field of traffic management and surveillance. With the rapid increase in the number of vehicles on roads, streets, and highways, the Intelligent Transport System (ITS) requirement has become inevitable. Vehicle detection and recognition systems have their roots embedded in ITS. It has been observed that different researchers have done much work in Day mode detection using Machine and Deep learning techniques. However, most of them faced difficulty in detection due to inadequate data, low illumination conditions, misclassification due to long shadows of vehicles, and testing on static frames. On the other hand, night vision detection is also facing difficulty due to low illumination conditions. The proposed work focuses on detecting moving vehicles in both day and night mode using a region-based deep learning technique called fast region based convolutional neural network (fast R-CNN). The proposed work has achieved promising results in situations like detection in the presence of long shadows, cloudy weather, detections in dense traffic during day vision, and pioneers the results in night mode conditions. Four evaluation parameters were used to test the system’s efficiency, mainly Recall, Accuracy, Precision, and Processing time. The proposed work achieved an overall average computation time of 0.59 s. Overall average Recall, accuracy, and precision of vehicle detection in day and night mode achieved were 98.44%, 94.20%, and 90%, respectively.