Vehicle Instance Research Articles

VisionDrive: Smart Traffic Analysis with YOLOv7 and DeepSORT

This research introduces a novel vehicle division mechanism intended for dynamic metropolitan settings. Modern technology, including deep learning, computer vision, and real-time warning systems, reduces traffic, enhances road safety, and effectively manages unanticipated situations. A strong vehicle instance segmentation model that can recognize cars in real-time from many sources is first developed using the YOLO technique. In the next round, the cars are tallied and a predetermined threshold is established. If there are more cars in the video or image than a preset threshold, the system will identify this and assume that traffic is heavy. During the last stage, users receive information on congestion analysis and anomaly identification through user-friendly interfaces. This article aims to modernize traffic control by merging cutting-edge technologies with all-encompassing techniques to deliver a responsive, safe, and efficient urban transportation experience. KEYWORDS: YOLOv7, DeepSORT, OpenCV, Object Detection, Multiclass Segmentation, Object Counting.

VEHICLE INSTANCE SEGMENTATION WITH ROTATED BOUNDING BOXES IN UAV IMAGES USING CNN

Abstract. Vehicle instance segmentation is a major but challenging task in aerial remote sensing applications. More importantly, the current majority methods use horizontal bounding boxes which does not tell much about the orientation of vehicles and often leads to inaccurate mask proposals due to high background to foreground pixel-ratio. Given that the orientation of vehicles is important for numerous applications like vehicle tracking, we introduce in this paper a deep neural network to detect and segment vehicles using rotated bounding boxes in aerial images. Our method demonstrates that rotated instance segmentation improves the mask predictions, especially when objects are not axis aligned or are touching. We evaluate our model on the ISPRS benchmark dataset and our newly introduced UAV dataset for vehicle segmentation and show that we can significantly improve the mask accuracy compared to instance segmentation using axis-aligned bounding boxes.

Automated visual surveying of vehicle heights to help measure the risk of overheight collisions using deep learning and view geometry

AbstractOverheight vehicle collisions continuously pose a serious threat to transportation infrastructure and public safety. This study proposed a vision‐based method for automatic vehicle height measurement using deep learning and view geometry. In this method, vehicle instances are first segmented from traffic surveillance video frames by exploiting mask region‐based convolutional neural network (Mask R‐CNN). Then, 3D bounding box on each vehicle instance is constructed using the obtained vehicle silhouette and three orthogonal vanishing points in the surveilled traffic scene. By doing so, the vertical edges of the constructed 3D bounding box are directly associated with the vehicle image height. Last, the vehicle's physical height is computed by referencing an object with a known height in the traffic scene using single view metrology. A field experiment was performed to evaluate the performance of the proposed method, leading to the mean and maximum errors of 3.6 and 6.6, 5.8 and 12.9, 4.4 and 8.1, and 9.2 and 18.5 cm for cars, buses, vans, and trucks, respectively. The experiment also demonstrated the ability of the method to overcome vehicle occlusion, shadow, and irregular appearance interferences in height estimation suffered by existing image‐based methods. The results signified the potential of the proposed method for overheight vehicle detection and collision warning in real traffic settings.

Part-Guided Attention Learning for Vehicle Instance Retrieval

Vehicle instance retrieval (IR) often requires one to recognize the fine-grained visual differences between vehicles. Besides the holistic appearance of vehicles which is easily affected by the viewpoint variation and distortion, vehicle parts also provide crucial cues to differentiate near-identical vehicles. Motivated by these observations, we introduce a <i>Part-Guided Attention Network</i> (PGAN) to pinpoint the prominent part regions and effectively combine the global and local information for discriminative feature learning. PGAN first detects the locations of different part components and salient regions regardless of the vehicle identity, which serves as the <i>bottom-up attention</i> to narrow down the possible searching regions. To estimate the importance of detected parts, we propose a <i>Part Attention Module</i> (PAM) to adaptively locate the most discriminative regions with high-attention weights and suppress the distraction of irrelevant parts with relatively low weights. The PAM is guided by the identification loss and therefore provides <i>top-down attention</i> that enables attention to be calculated at the level of car parts and other salient regions. Finally, we aggregate the global appearance and local features together to improve the feature performance further. The PGAN combines part-guided bottom-up and top-down attention, global and local visual features in an end-to-end framework. Extensive experiments demonstrate that the proposed method achieves new state-of-the-art vehicle IR performance on four large-scale benchmark datasets.<xref ref-type="fn" rid="fn1">¹</xref>

Vehicle Instance Segmentation From Aerial Image and Video Using a Multitask Learning Residual Fully Convolutional Network

Object detection and semantic segmentation are two main themes in object retrieval from high-resolution remote sensing images, which have recently achieved remarkable performance by surfing the wave of deep learning and, more notably, convolutional neural networks (CNNs). In this paper, we are interested in a novel, more challenging problem of vehicle instance segmentation, which entails identifying, at a pixel-level, where the vehicles appear as well as associating each pixel with a physical instance of a vehicle. In contrast, vehicle detection and semantic segmentation each only concern one of the two. We propose to tackle this problem with a semantic boundary-aware multi-task learning network. More specifically, we utilize the philosophy of residual learning (ResNet) to construct a fully convolutional network that is capable of harnessing multi-level contextual feature representations learned from different residual blocks. We theoretically analyze and discuss why residual networks can produce better probability maps for pixel-wise segmentation tasks. Then, based on this network architecture, we propose a unified multi-task learning network that can simultaneously learn two complementary tasks, namely, segmenting vehicle regions and detecting semantic boundaries. The latter subproblem is helpful for differentiating closely spaced vehicles, which are usually not correctly separated into instances. Currently, datasets with pixel-wise annotation for vehicle extraction are ISPRS dataset and IEEE GRSS DFC2015 dataset over Zeebrugge, which specializes in semantic segmentation. Therefore, we built a new, more challenging dataset for vehicle instance segmentation, called the Busy Parking Lot UAV Video dataset, and we make our dataset available at http://www.sipeo.bgu.tum.de/download so that it can be used to benchmark future vehicle instance segmentation algorithms.