MS-YOLO: Object Detection Based on YOLOv5 Optimized Fusion Millimeter-Wave Radar and Machine Vision

Song, Yunyun; Xie, Zhen; Wang, Xinwei; Zou, Yingquan

doi:10.1109/jsen.2022.3167251

Cited by 77 publications

(24 citation statements)

References 31 publications

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“…You Only Look Once version 5 (YOLOv5) is a state-of-the-art object-detection model known for its efficiency and high performance [51][52][53]. At its core, YOLOv5 is designed to identify and localize multiple objects in images or video feeds, executing these tasks with high accuracy.…”

Section: Object Detection Using Yolov5 Architecturementioning

confidence: 99%

Improving SLAM Techniques with Integrated Multi-Sensor Fusion for 3D Reconstruction

Cai,

Ou,

Qin

2024

Sensors

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Simultaneous Localization and Mapping (SLAM) poses distinct challenges, especially in settings with variable elements, which demand the integration of multiple sensors to ensure robustness. This study addresses these issues by integrating advanced technologies like LiDAR-inertial odometry (LIO), visual-inertial odometry (VIO), and sophisticated Inertial Measurement Unit (IMU) preintegration methods. These integrations enhance the robustness and reliability of the SLAM process for precise mapping of complex environments. Additionally, incorporating an object-detection network aids in identifying and excluding transient objects such as pedestrians and vehicles, essential for maintaining the integrity and accuracy of environmental mapping. The object-detection network features a lightweight design and swift performance, enabling real-time analysis without significant resource utilization. Our approach focuses on harmoniously blending these techniques to yield superior mapping outcomes in complex scenarios. The effectiveness of our proposed methods is substantiated through experimental evaluation, demonstrating their capability to produce more reliable and precise maps in environments with variable elements. The results indicate improvements in autonomous navigation and mapping, providing a practical solution for SLAM in challenging and dynamic settings.

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Section: Object Detection Using Yolov5 Architecturementioning

confidence: 99%

Improving SLAM Techniques with Integrated Multi-Sensor Fusion for 3D Reconstruction

Cai,

Ou,

Qin

2024

Sensors

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“…Onboard camera feeds are sliced into frames and the frames are fed into You Only Look Once (YOLO) framework which is the popular network used for object detection [42] due to their speed, accuracy, easy to train, validate and deployment. YOLO Model combines bounding box prediction with class labels in an end-to-end differentiable network.…”

Section: A Surrounding Vehicles Detectionmentioning

confidence: 99%

Surrounding Vehicle Motion Prediction in Non-Lane-Based Environments for ADAS Enhancement

Antony,

2023

IEEE Access

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The significance of forecasting the behavior of road agents is on the rise, particularly in Advanced Driver Assistance Systems (ADAS) enhancement. Predicting road agents' intentions holds paramount importance for Autonomous vehicles, especially considering the forthcoming coexistence of ADAS systems with heterogeneous road entities within urban roadways. The behavioral attributes of non-lane based traffic, featuring a mix of various elements, are prevalent not only in urban scenarios but also in unstructured environments. This research aims to predict the movement patterns of surrounding vehicles in non-lane based environments. This study captures the surrounding vehicles' intent to utilize tight lateral spaces in non-lane based environments through the variations of lateral descriptor values. The investigation takes into account several factors, including leveraging contextual cues, retaining spatial data related to neighboring vehicles, and identifying driving patterns. To achieve these objectives, a hybrid model is introduced, combining a modified structured Long Short Term Memory (LSTM) with lateral descriptor based uncertainty estimation on top of established detection and tracking algorithms. This integration enhances the ability to capture spatial attributes of neighboring vehicles along with the assessment of traffic conflict indicators with lateral descriptor contextual cues. The methodology is evaluated across two distinct datasets: one simulating scenarios of neighboring vehicles within well-defined urban road setups, and the other representing non-structured environments. The empirical findings highlight the effectiveness of the proposed method, showcasing an impressive 24.69% enhancement in prediction accuracy compared to baseline models with 5-seconds prediction horizon.

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“…In the field of TD, You Only Look Once (YOLO) and its derived network structures have achieved better detection results. Among them, the You Only Look Once Version 4 (YOLOv4) algorithm has received wide attention for its efficient detection speed and good accuracy [3][4]. Real-time TD of scenic landscapes is challenging due to various factors, including light variations, occlusion problems, complex backgrounds, and diverse target types.…”

Section: Introductionmentioning

confidence: 99%

Real-time Target Detection System in Scenic Landscape Based on Improved YOLOv4 Algorithm

Pan,

Zhao,

Sun

2024

IJCAI

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With the rapid development of computer vision technology, the use of real-time target detection systems in scenic landscape management and services is increasingly widespread. To enhance the precision and efficiency of real-time target detection in scenic landscapes, this research integrates the fourth version of the You Only Look Once (YOLO) algorithm to construct an optimized real-time target detection system is introduced for scenic landscapes. First, adaptive spatial feature fusion to enhance the fourth version of the You Only Look Once algorithm. Then, the optimized algorithm was combined with OpenCV library, Python OS library, and other hardware and software to design a real-time image recognition system for scenic landscapes. The study results indicated that the proposed optimized algorithm had better recognition performance, and its precision value, recall rate, and F1 value were as high as 0.96, 0.97, and 0.98, respectively. The recognition system, which was developed using an optimization algorithm, demonstrated excellent practical application effect. It displayed stable system operation under four natural landscapes: sunrise, sea of clouds, maple forest, and stone monument, with a stability performance of 0.92, 0.93, 0.92, and 0.94, respectively. Moreover, the system operated remarkably fast, with low operational times of 2.3 s, 0.8 s, 2.9 s, and 1.2 s under these landscapes. In conclusion, the research institute's target detection algorithm has demonstrated excellent performance. Utilizing this algorithm in the detection system can offer technical aid for managing and intelligently detecting scenic landscape images.Povzetek: Raziskava predstavlja izboljšan sistem za zaznavanje tarč v realnem času v slikovitih krajinskih območjih, temelječ na algoritmu YOLOv4, s prilagodljivo prostorsko združitvijo značilnosti in uporabo knjižnic OpenCV in Python OS.

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MS-YOLO: Object Detection Based on YOLOv5 Optimized Fusion Millimeter-Wave Radar and Machine Vision

Cited by 77 publications

References 31 publications

Improving SLAM Techniques with Integrated Multi-Sensor Fusion for 3D Reconstruction

Improving SLAM Techniques with Integrated Multi-Sensor Fusion for 3D Reconstruction

Surrounding Vehicle Motion Prediction in Non-Lane-Based Environments for ADAS Enhancement

Real-time Target Detection System in Scenic Landscape Based on Improved YOLOv4 Algorithm

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