A Hybrid YOLOv4 and Particle Filter Based Robotic Arm Grabbing System in Nonlinear and Non-Gaussian Environment

Gao, Mingyu; Cai, Qinyu; Zheng, Bowen; Shi, Jie; Ni, Zhihao; Wang, Junfan; Lin, Huipin

doi:10.3390/electronics10101140

Cited by 13 publications

(7 citation statements)

References 31 publications

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“…Our result was similar to the study by Gao et al facilitating a robotic arm grasping system in nonlinear and non-Gaussian environment detection using labeling objects on the boundary, with a YOLOv4 range of 96.70% to 99.50% AP. Therefore, YOLOv4 was chosen rather than YOLOv3 and YOLOv5 [ 81 ].…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images

Ardhianto

Subiakto

Lin

et al. 2022

Sensors

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Foot progression angle (FPA) analysis is one of the core methods to detect gait pathologies as basic information to prevent foot injury from excessive in-toeing and out-toeing. Deep learning-based object detection can assist in measuring the FPA through plantar pressure images. This study aims to establish a precision model for determining the FPA. The precision detection of FPA can provide information with in-toeing, out-toeing, and rearfoot kinematics to evaluate the effect of physical therapy programs on knee pain and knee osteoarthritis. We analyzed a total of 1424 plantar images with three different You Only Look Once (YOLO) networks: YOLO v3, v4, and v5x, to obtain a suitable model for FPA detection. YOLOv4 showed higher performance of the profile-box, with average precision in the left foot of 100.00% and the right foot of 99.78%, respectively. Besides, in detecting the foot angle-box, the ground-truth has similar results with YOLOv4 (5.58 ± 0.10° vs. 5.86 ± 0.09°, p = 0.013). In contrast, there was a significant difference in FPA between ground-truth vs. YOLOv3 (5.58 ± 0.10° vs. 6.07 ± 0.06°, p < 0.001), and ground-truth vs. YOLOv5x (5.58 ± 0.10° vs. 6.75 ± 0.06°, p < 0.001). This result implies that deep learning with YOLOv4 can enhance the detection of FPA.

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Section: Discussionmentioning

confidence: 99%

A Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images

Ardhianto

Subiakto

Lin

et al. 2022

Sensors

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“…The model fitting curve can finally be obtained by learning the parameters of the regression equation through a lot of training. The annotation, confidence, and category probability calculation of the end-to-end bounding boxes of the targets can be completed only by inputting the picture once [21]. YOLO has high recognition efficiency and is widely used in real-time detection.…”

Section: Yolov4 Target Detection Algorithmmentioning

confidence: 99%

High Inclusiveness and Accuracy Motion Blur Real-Time Gesture Recognition Based on YOLOv4 Model Combined Attention Mechanism and DeblurGanv2

et al. 2021

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The combination of gesture recognition and aerospace exploration robots can realize the efficient non-contact control of the robots. In the harsh aerospace environment, the captured gesture images are usually blurred and damaged inevitably. The motion blurred images not only cause part of the transmitted information to be lost, but also affect the effect of neural network training in the later stage. To improve the speed and accuracy of motion blurred gestures recognition, the algorithm of YOLOv4 (You Only Look Once, vision 4) is studied from the two aspects of motion blurred image processing and model optimization. The DeblurGanv2 is employed to remove the motion blur of the gestures in YOLOv4 network input pictures. In terms of model structure, the K-means++ algorithm is used to cluster the priori boxes for obtaining the more appropriate size parameters of the priori boxes. The CBAM attention mechanism and SPP (spatial pyramid pooling layer) structure are added to YOLOv4 model to improve the efficiency of network learning. The dataset for network training is designed for the human–computer interaction in the aerospace space. To reduce the redundant features of the captured images and enhance the effect of model training, the Wiener filter and bilateral filter are superimposed on the blurred images in the dataset to simply remove the motion blur. The augmentation of the model is executed by imitating different environments. A YOLOv4-gesture model is built, which collaborates with K-means++ algorithm, the CBAM and SPP mechanism. A DeblurGanv2 model is built to process the input images of the YOLOv4 target recognition. The YOLOv4-motion-blur-gesture model is composed of the YOLOv4-gesture and the DeblurGanv2. The augmented and enhanced gesture data set is used to simulate the model training. The experimental results demonstrate that the YOLOv4-motion-blur-gesture model has relatively better performance. The proposed model has the high inclusiveness and accuracy recognition effect in the real-time interaction of motion blur gestures, it improves the network training speed by 30%, the target detection accuracy by 10%, and the value of mAP by about 10%. The constructed YOLOv4-motion-blur-gesture model has a stable performance. It can not only meet the real-time human–computer interaction in aerospace space under real-time complex conditions, but also can be applied to other application environments under complex backgrounds requiring real-time detection.

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“…Able to be trained with a single conventional GPU, YOLOv4 is optimized for simple configuration. YOLOv4 has been utilized and applied for a variety of purposes, such as melanoma lesion detection (Albahli et al, 2020), robotics (Gao et al, 2021), and surveillance (Kumar et al, 2020). Often applied for real-time detection and constant surveillance, its implementation in the domain of mosquito habitat surveillance and detection proves advantageous for active mosquito disease prevention efforts.…”

Section: Yolov4mentioning

confidence: 99%

Novel Approach to Autonomous Mosquito Habitat Detection using Satellite Imagery and Convolutional Neural Networks for Disease Risk Mapping

Elango

Ramachandran

2021

Preprint

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Mosquitoes are known vectors for disease transmission that cause over one million deaths globally each year. The majority of natural mosquito habitats are areas containing standing water such as ponds, lakes, and marshes. These habitats are challenging to detect using conventional ground-based technology on a macro scale. Contemporary approaches, such as drones, UAVs, and other aerial imaging technology are costly when implemented. Multispectral imaging technology such as Lidar is most accurate on a finer spatial scale whereas the proposed convolutional neural network(CNN) approach can be applied for disease risk mapping and further guide preventative efforts on a more global scale. By assessing the performance of autonomous mosquito habitat detection technology, the transmission of mosquito-borne diseases can be prevented in a costeffective manner. This approach aims to identify the spatiotemporal distribution of mosquito habitats in extensive areas that are difficult to survey using groundbased technology by employing computer vision on satellite imagery for proof of concept. The research presents an evaluation and the results of 3 different CNN models to determine their accuracy of predicting largescale mosquito habitats. For this approach, a dataset was constructed utilizing Google Earth satellite imagery containing a variety of geographical features in residential neighborhoods as well as cities across the world. Larger land cover variables such as ponds/lakes, inlets, and rivers were utilized to classify mosquito habitats while minute sites such as puddles, footprints, and additional human-produced mosquito habitats were omitted for higher accuracy on a larger scale. Using the dataset, multiple CNN networks were trained and evaluated for accuracy of habitat prediction. Utilizing a CNN-based approach on readily available satellite imagery is cost-effective and scalable, unlike most aerial imaging technology. Testing revealed that YOLOv4 obtained greater accuracy in mosquito habitat detection than YOLOR or YOLOv5 for identifying large-scale mosquito habitats. YOLOv4 is found to be a viable method for global mosquito habitat detection and surveillance.

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A Hybrid YOLOv4 and Particle Filter Based Robotic Arm Grabbing System in Nonlinear and Non-Gaussian Environment

Cited by 13 publications

References 31 publications

A Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images

A Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images

High Inclusiveness and Accuracy Motion Blur Real-Time Gesture Recognition Based on YOLOv4 Model Combined Attention Mechanism and DeblurGanv2

Novel Approach to Autonomous Mosquito Habitat Detection using Satellite Imagery and Convolutional Neural Networks for Disease Risk Mapping

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