Deep learning-based classification and detection algorithms have emerged as a powerful tool for vehicle detection in intelligent transportation systems. The limitations of the number of high-quality labeled training samples makes the single vehicle detection methods incapable of accomplishing acceptable accuracy in road vehicle detection. This paper presents detection and classification of vehicles on publicly available datasets by utilizing the YOLO-v5 architecture. This paper’s findings utilize the concept of transfer learning through fine tuning the weights of the pre-trained YOLO-v5 architecture. To employ the concept of transfer learning, extensive data sets of images and videos of the congested traffic patterns were collected by the authors. These datasets were made more comprehensive by pointing various attributes, for instance high- and low-density traffic patterns, occlusions, and different weather circumstances. All of these gathered datasets were manually annotated. Ultimately, the improved YOLO-v5 structure becomes accustomed to any difficult traffic patterns. By fine-tuning the pre-trained network through our datasets, our proposed YOLO-v5 has exceeded several other traditional vehicle detection methods in terms of detection accuracy and execution time. Detailed simulations performed on the PKU, COCO, and DAWN datasets demonstrate the effectiveness of the proposed method in various challenging situations.
The underwater friction stir processing is used for development of aluminum metal matrix composite (AA2219-Y2O3) foam. For development of foam, holes with different diameter in the mid thickness of plate were filled with a mixture of TiH2 and aluminum powder and underwater friction stir processing was used to mix this mixture in aluminum metal matrix composite. Then precursors extracted from the processed zone and heated upto 650°C in a furnace for development of foam. The effect of diameter of hole, number of passes and the tool rotation direction has been studied on the foam cell size and static and dynamic compressive behavior of the foam. It is found that as the diameter of hole increases, the size of pores increases. The distribution of pores is better with higher number of passes and increasing the hole diameter. The quality of foam further improves by reversing the tool rotation direction. The developed foam has different pore size varies from 0.7 to 2.7 mm depends on the FSP parameters. Based on the size of pores and their distribution the relative density ranges from 0.1 to 0.78. The foam produced with 4 mm hole diameter has best static and dynamic compression properties.
Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehicles’ license plates in images is a critical step that has a substantial impact on any ALPD system’s recognition rate. In this paper, we develop an efficient license plate detecting technique through the intelligent combination of Faster R-CNN along with digital image processing techniques. The proposed algorithm initially detects vehicle(s) in the input image through Faster R-CNN. Later, the located vehicle is analyzed by a robust License Plate Localization Module (LPLM). The LPLM module primarily uses color segmentation and processes the HSV image to detect the license plate in the input image. Moreover, the LPLM module employs morphological filtering and dimension analysis to find the license plate. Detailed trials on challenging PKU datasets demonstrate that the proposed method outperforms few recently developed methods by producing high license plates detection accuracy in much less execution time. The proposed work demonstrates a great feasibility for security and target detection applications.
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