Browsing by Subject "YOLOv5"

Now showing 1 - 3 of 3
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    Attention Mechanism Improves YOLOv5x for Detecting Vehicles on Surveillance Videos
    (IEEE, 2022-10) Qui, Mei; Christopher, Lauren Ann; Chein, Stanley; Chen, Yaobin; Electrical and Computer Engineering, School of Engineering and Technology
    Vehicle detection accuracy on surveillance videos is heavily restricted by camera angles, low lighting conditions, low visibility caused by harsh weather, and serious occlusions. For the full 24/7 operation, the Intelligent Transportation Services (ITS) are expected to perform well on all the categories of the target detections in the environment. Unfortunately, most existing datasets do not cover all these difficult conditions. Moreover, the state-of-the-art Deep Learning detector performance decreases for these difficult conditions. This paper reports on the training of an object detection system using a range of traffic scenarios: sunny, rainy, snowy, one-side road, two-side road, complex road structures with occlusions, heavy traffic with congestion, light traffic, and reduced traffic at night. The state-of-the-art object detector of YOLOv5x is used for vehicle detection and is fine-tuned on this new diverse dataset through transfer learning. Transfer learning freezes the backbone network while training the remaining fully connected network. To further improve the detection performance, we added two convolutional block attention modules (CBAM) to the neck as our proposed system: 2xCBAM-YOLOv5. Several experiments refined the number of CBAMs and the placement of these modules to optimize performance. Doing transfer learning alone, the mean Average Precision(mAP) on the test data improves from 75.9% to 78.9%. After transfer learning, ablations were done on YOLOv5x combined with the new CBAMs. The resulting mAP reaches 85.0%, while precision improves from 82.3% to 88.2%, recall improves from 72.3% to 80.4% and F1-score improves from 0.77 to 0.841 compared with transfer learning alone. This new architecture provides an overall improvement for ITS traffic surveillance applications.
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    BIoU: An Improved Bounding Box Regression for Object Detection
    (MDPI, 2022-09-28) Ravi, Niranjan; Naqvi, Sami; El-Sharkawy, Mohamed; Electrical and Computer Engineering, School of Engineering and Technology
    Object detection is a predominant challenge in computer vision and image processing to detect instances of objects of various classes within an image or video. Recently, a new domain of vehicular platforms, e-scooters, has been widely used across domestic and urban environments. The driving behavior of e-scooter users significantly differs from other vehicles on the road, and their interactions with pedestrians are also increasing. To ensure pedestrian safety and develop an efficient traffic monitoring system, a reliable object detection system for e-scooters is required. However, existing object detectors based on IoU loss functions suffer various drawbacks when dealing with densely packed objects or inaccurate predictions. To address this problem, a new loss function, balanced-IoU (BIoU), is proposed in this article. This loss function considers the parameterized distance between the centers and the minimum and maximum edges of the bounding boxes to address the localization problem. With the help of synthetic data, a simulation experiment was carried out to analyze the bounding box regression of various losses. Extensive experiments have been carried out on a two-stage object detector, MASK_RCNN, and single-stage object detectors such as YOLOv5n6, YOLOv5x on Microsoft Common Objects in Context, SKU110k, and our custom e-scooter dataset. The proposed loss function demonstrated an increment of 3.70% at 𝐴𝑃𝑆 on the COCO dataset, 6.20% at AP55 on SKU110k, and 9.03% at AP80 of the custom e-scooter dataset.
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    Real-Time Embedded Implementation of Improved Object Detector for Resource-Constrained Devices
    (MDPI, 2022-04-13) Ravi, Niranjan; El-Sharkawy, Mohamed; Electrical and Computer Engineering, School of Engineering and Technology
    Artificial intelligence (A.I.) has revolutionised a wide range of human activities, including the accelerated development of autonomous vehicles. Self-navigating delivery robots are recent trends in A.I. applications such as multitarget object detection, image classification, and segmentation to tackle sociotechnical challenges, including the development of autonomous driving vehicles, surveillance systems, intelligent transportation, and smart traffic monitoring systems. In recent years, object detection and its deployment on embedded edge devices have seen a rise in interest compared to other perception tasks. Embedded edge devices have limited computing power, which impedes the deployment of efficient detection algorithms in resource-constrained environments. To improve on-board computational latency, edge devices often sacrifice performance, creating the need for highly efficient A.I. models. This research examines existing loss metrics and their weaknesses, and proposes an improved loss metric that can address the bounding box regression problem. Enhanced metrics were implemented in an ultraefficient YOLOv5 network and tested on the targeted datasets. The latest version of the PyTorch framework was incorporated in model development. The model was further deployed using the ROS 2 framework running on NVIDIA Jetson Xavier NX, an embedded development platform, to conduct the experiment in real time.