Abstract
The paper presents a pragmatic approach towards developing and analysing a GUI-based system performance towards detecting and counting tracked multi-type vehicles in mixed traffic conditions using the improved you only look once v3 (YOLOv3) model. It addresses the issue related to accurately localising smaller vehicles in an occluded scenario and environmental conditions. In the training phase of the proposed work stochastic gradient descent (SGD) optimises the network. Further, to detect small vehicles four different scales feature mapping are performed to extract more fine-grained features for accurate detection, followed by concatenating upsampled layers with lower layers to improve the detection performance of low-resolution images. The intersection over union (IoU) approach detects every vehicle in the subsequent frame by assigning a unique ID to classify detected vehicles into five specific classes bus, truck, car, motorbike, and bicycle. Further, the SORT algorithm tracks and counts the detected vehicles. Experimental result on the common objects in context (COCO) dataset shows an improvement in the mean average precision (mAP) by 11% compared to the existing YOLOv3 technique.
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