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.

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.

License plate recognition (LPR) is an integral part of the current intelligent systems that are developed to locate and identify various objects. Unfortunately, the LPR is a challenging task due to various factors, such as the numerous shapes and designs of the LPs, the non-following of standard LP templates, irregular outlines, angle variations, and occlusion. These factors drastically influence the LP appearance and significantly challenge the detection and recognition abilities of state-of-the-art detection and recognition algorithms. However, recent rising trends in the development of machine learning algorithms have yielded encouraging solutions. This paper presents a novel LPR method to address the aforesaid issues. The proposed method is composed of three distinct but interconnected steps. First, a vehicle that appears in an input image is detected using the Faster RCNN. Next, the LP area is located within the detected vehicle by using morphological operations. Finally, license plate recognition is accomplished using the deep learning network. Detailed simulations performed on the PKU, AOLP, and CCPD databases indicate that our developed approach produces mean license plate recognition accuracy of 99%, 96.0231%, and 98.7000% on the aforesaid databases.

Recent advancements demonstrate the application of machine learning techniques to radar signal processing, particularly the use of neural networks in 1-D radar data to assist Constant False Alarm Rate (CFAR) based target detection. However, the efficacy of using neural networks alone on 2-D data for target extraction remains to be explored. This research aims to develop a novel approach of designing a Convolution Neural Network (CNN) based target detection system, for real radar data. The first step in this approach filters out the noisy images. While in the second step the CNN-based detector uses both radar data axes (fast-time and slow-time), thus utilizing additional information for better detection of targets even with low SNR. Moreover, the same CNN, in conjunction with a region-based convolutional network, exploits micro-Doppler features of rotary-wing aircraft in radar returns and uses them to further classify detected targets as fixed-wing or rotary-wing aircraft. To the best of our knowledge, no generalized methodology exists to address these two issues together. This paper also introduces a labeled range-Doppler images dataset obtained from real-world Air Traffic Control (ATC) Radar data. Experimental result demonstrates the superior performance of the proposed technique over 1-D and 2-D CFAR detectors.

Foreign object debris (FOD) is any undesired and unintended object placed or found in the specific vicinity of an aircraft (runway/ taxiway) that can cause damage to aircraft or harm personnel on board such as twisted metal strips, screws, nuts, and bolts, depleted concrete runway pieces, stones, pebbles and stationery items. To avoid FOD damages, all airport/ aviation organizations have deployed some sort of FOD prevention procedure. However, automatic FOD detection systems are still scarce owing to the inevitable reliance on human experts that lead to unavoidable human errors. Around 60% of FOD consists of metal which is the most deteriorating for an aircraft. Therefore, the implementation of material recognition techniques for FOD classification through Deep Convolutional Neural Networks (DCNN) is more important than FOD object detection as FOD could be of any shape, size or color. This paper developed a DCNN algorithm for FOD material classification with high accuracy for all included material classes (i.e., metal, concrete, plastic) in general and metal in particular. For this, a new dataset is introduced that consists of 2481 images taken on an operational airport runway in varying illumination and weather conditions. Through extensive testing, it was found that InceptionV3 is the best performing model with 18% improvement in metal recognition, and 11% improvement in average accuracy for all included classes.

A significant amount of satellite imaging data is now easily available due to the continued development of remote sensing (RS) technology. Enabling the successful application of RS in real-world settings requires efficient and scalable solutions to extend their use in multidisciplinary areas. The goal of quick analysis and precise classification in Remote Sensing Imaging (RSI) is often accomplished by utilizing approaches based on deep Convolution Neural Networks (CNNs). This research offers a unique snapshot-based residual network (SnapResNet) that consists of fully connected layers (FC-1024), batch normalization (BN), L2 regularization, dropout layers, dense layer, and data augmentation. Architectural changes overcome the inter-class similarity problem while data augmentation resolves the problem of imbalanced classes. Moreover, the snapshot ensemble technique is utilized to prevent over-fitting, thereby further improving the network’s performance. The proposed SnapResNet152 model employs the most challenging Large-Scale Cloud Images Dataset for Meteorology Research (LSCIDMR), having 10 classes with thousands of high-resolution images and classifying them into respective classes. The developed model outperforms the existing deep learning-based algorithms (e.g., AlexNet, VGG-19, ResNet101, and EfficientNet) and achieves an overall accuracy of 97.25%.