Object Detection Algorithm for Wheeled Mobile Robot Based on an Improved YOLOv4

Hu, Yanxin; Liu, Gang; Chen, Zhiyu; Guo, Jianwei

doi:10.3390/app12094769

Cited by 11 publications

(7 citation statements)

References 40 publications

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“…Furthermore, Table 4 shows that our algorithm provides accurate results when compared to other object-detection algorithms in similar mobile robot tasks. Some results of similar studies on environment recognition include Tiny-YOLO [76] with and improved YOLOv4 [78] with 67.6% and 86.8% of mAP, respectively. In comparison, our algorithm offers better accuracy, and the number of training images is more than 10 times lower.…”

Section: Discussionmentioning

confidence: 88%

Simultaneous Object Detection and Distance Estimation for Indoor Autonomous Vehicles

Azurmendi,

Zulueta,

Lopez-Guede

et al. 2023

Electronics

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Object detection is an essential and impactful technology in various fields due to its ability to automatically locate and identify objects in images or videos. In addition, object-distance estimation is a fundamental problem in 3D vision and scene perception. In this paper, we propose a simultaneous object-detection and distance-estimation algorithm based on YOLOv5 for obstacle detection in indoor autonomous vehicles. This method estimates the distances to the desired obstacles using a single monocular camera that does not require calibration. On the one hand, we train the algorithm with the KITTI dataset, which is an autonomous driving vision dataset that provides labels for object detection and distance prediction. On the other hand, we collect and label 100 images from a custom environment. Then, we apply data augmentation and transfer learning to generate a fast, accurate, and cost-effective model for the custom environment. The results show a performance of mAP0.5:0.95 of more than 75% for object detection and 0.71 m of mean absolute error in distance prediction, which are easily scalable with the labeling of a larger amount of data. Finally, we compare our method with other similar state-of-the-art approaches.

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Section: Discussionmentioning

confidence: 88%

Simultaneous Object Detection and Distance Estimation for Indoor Autonomous Vehicles

Azurmendi,

Zulueta,

Lopez-Guede

et al. 2023

Electronics

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show abstract

“…Object detection is essential not only for autonomous cars but also for smaller vehicles like autonomous robots. An approach for object detection for such small robots has been introduced by Hu et al [107]. In their article, the authors presented a novel object detection algorithm based on YOLOv4 and GhostNet, performing training on a centralized server and then deploying the trained model to run directly on an edge device, in this case, Jetson TX2.…”

Section: Autonomous and Intelligence-assisted Vehiclesmentioning

confidence: 99%

Combining Machine Learning and Edge Computing: Opportunities, Challenges, Platforms, Frameworks, and Use Cases

Grzesik,

Mrozek

2024

Electronics

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In recent years, we have been observing the rapid growth and adoption of IoT-based systems, enhancing multiple areas of our lives. Concurrently, the utilization of machine learning techniques has surged, often for similar use cases as those seen in IoT systems. In this survey, we aim to focus on the combination of machine learning and the edge computing paradigm. The presented research commences with the topic of edge computing, its benefits, such as reduced data transmission, improved scalability, and reduced latency, as well as the challenges associated with this computing paradigm, like energy consumption, constrained devices, security, and device fleet management. It then presents the motivations behind the combination of machine learning and edge computing, such as the availability of more powerful edge devices, improving data privacy, reducing latency, or lowering reliance on centralized services. Then, it describes several edge computing platforms, with a focus on their capability to enable edge intelligence workflows. It also reviews the currently available edge intelligence frameworks and libraries, such as TensorFlow Lite or PyTorch Mobile. Afterward, the paper focuses on the existing use cases for edge intelligence in areas like industrial applications, healthcare applications, smart cities, environmental monitoring, or autonomous vehicles.

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“…The initial Anchor can be set in the dataset, and when the network is trained, the training network outputs the prediction frame based on the Anchor, and the loss can be calculated by comparing it with the ground truth frame; after the loss is obtained, the network is updated with feedback, and the network parameters are updated by iterating through each layer of the network. The initial anchor points in YOLOv3 [35] and YOLOv4 [36] can be set using a specific K-means algorithm setup procedure. However, the Anchor of the YOLOv5 network differs from the above two versions in that the Anchor is written directly into the overall code of the network in the YOLOv5 network, and the optimal anchor frame values of the YOLOv5 network are calculated adaptively each time the dataset is used for training.…”

Section: Adaptive Anchor Frame Calculationmentioning

confidence: 99%

Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes

Zhang

Wang

2022

Applied Sciences

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When building a map of a dynamic environment, simultaneous localization and mapping systems have problems such as poor robustness and inaccurate pose estimation. This paper proposes a new mapping method based on the ORB-SLAM2 algorithm combined with the YOLOv5 network. First, the YOLOv5 network of the tracing thread is used to detect dynamic objects of each frame, and to get keyframes with detection of dynamic information. Second, the dynamic objects of each image frame are detected using the YOLOv5 network, and the detected dynamic points are rejected. Finally, the global map is constructed using the keyframes after eliminating the highly dynamic objects. The test results using the TUM dataset show that when the map is constructed in a dynamic environment, compared with the ORB-SLAM2 algorithm, the absolute trajectory error of our algorithm is reduced by 97.8%, and the relative positional error is reduced by 59.7%. The average time consumed to track each image frame is improved by 94.7% compared to DynaSLAM. In terms of algorithmic real-time performance, this paper’s algorithm is significantly better than the comparable dynamic SLAM map-building algorithm DynaSLAM.

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Object Detection Algorithm for Wheeled Mobile Robot Based on an Improved YOLOv4

Cited by 11 publications

References 40 publications

Simultaneous Object Detection and Distance Estimation for Indoor Autonomous Vehicles

Simultaneous Object Detection and Distance Estimation for Indoor Autonomous Vehicles

Combining Machine Learning and Edge Computing: Opportunities, Challenges, Platforms, Frameworks, and Use Cases

Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes

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