Bi-path Combination YOLO for Real-time Few-shot Object Detection

Xia, Ruiyang; Li, Guoquan; Huang, Zhengwen; Meng, Hongying; Pang, Yu

doi:10.1016/j.patrec.2022.11.025

Cited by 13 publications

(7 citation statements)

References 38 publications

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“…Wang et al, 2022;D. Wang et al, 2021;Xia et al, 2023). One machine learning method, transfer learning, uses a previously trained model to address unrelated issues.…”

Section: Methodsmentioning

confidence: 99%

Computer Vision-Assisted Object Detection and Handling Framework for Robotic Arm Design Using YOLOV5

Maideen,

Mohanarathinam

2023

ADCAIJ

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In recent years, there has been a surge in scientific research using computer vision and robots for precision agriculture. Productivity has increased significantly, and the need for human labor in agriculture has been dramatically reduced owing to technological and mechanical advancements. However, most current apple identification algorithms cannot distinguish between green and red apples on a diverse agricultural field, obscured by tree branches and other apples. A novel and practical target detection approach for robots, using the YOLOV5 framework is presented, in line with the need to recognize apples automatically. Robotic end effectors have been integrated into a Raspberry Pi 4B computer, where the YOLOV5 model has been trained, tested, and deployed. The image was taken with an 8-megapixel camera that uses the camera serial interface (CSI) protocol. To speed up the model creation process, researchers use a graphical processing computer to label and preprocess test images before utilizing them. Using YOLOV5, a computer vision system-assisted framework aids in the design of robotic arms capable of detecting and manipulating objects. The deployed model has performed very well on both red and green apples, with ROC values of 0.98 and 0.9488, respectively. The developed model has achieved a high F1 score with 91.43 for green apples and 89.95 for red apples. The experimental findings showed that robotics are at the forefront of technological advancement because of the rising need for productivity, eliminating monotonous work, and protecting the operator and the environment. The same discerning can be applied to agricultural robots, which have the potential to improve productivity, safety, and profit margins for farmers while reducing their impact on the environment. The system’s potential could be seen in an assortment of fields, including sophisticated object detection, nuanced manipulation, multi-robot collaboration, and field deployment.

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“…Wang et al, 2022;D. Wang et al, 2021;Xia et al, 2023). One machine learning method, transfer learning, uses a previously trained model to address unrelated issues.…”

Section: Methodsmentioning

confidence: 99%

Computer Vision-Assisted Object Detection and Handling Framework for Robotic Arm Design Using YOLOV5

Maideen,

Mohanarathinam

2023

ADCAIJ

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“…The mean attained precision rate for test data was 92.34%. Some further applications of YOLO include road crack detection [ 32 ], semi-supervised YOLO for generic object detection [ 56 ], head detection [ 48 ], defect detection [ 28 ], YOLO-based a few-shot model [ 50 ], detecting small targets in infrared remote sensing [ 18 ], vehichle detection [ 5 ], traffic sign detection [ 52 ], colon cancer detection [ 24 ], and cattle body detection [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

“… 98% [ 28 ] 2023 Vu et al YOLO In total, 400 photographs were taken: 200 of broken boxes and 200 of intact ones. 78.6% [ 50 ] 2023 Xia et al BC-YOLO PASCAL VOC 2007 and MS COCO 2014 datasets were used 43.9% [ 18 ] 2023 Li and Shen YOLOSR-IST Dataset from infrared image sequences (IRIS) and single-frame Infrared small target (SIRST) 99.2% [ 5 ] 2023 Bie et al YOLOv5n-L The BDD100K dataset was utilised. 67.8% [ 52 ] 2023 Yao et al YOLOv4-Tiny The CSUST Chinese Traffic Sign Detection Benchmark contains 15,734 pictures, including approximately 40,000 traffic signs of varying sizes.…”

Section: Related Workmentioning

confidence: 99%

Optimized deep learning vision system for human action recognition from drone images

Samma

Sama

2023

Multimed Tools Appl

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There are several benefits to constructing a lightweight vision system that is implemented directly on limited hardware devices. Most deep learning-based computer vision systems, such as YOLO (You Only Look Once), use computationally expensive backbone feature extractor networks, such as ResNet and Inception network. To address the issue of network complexity, researchers created SqueezeNet, an alternative compressed and diminutive network. However, SqueezeNet was trained to recognize 1000 unique objects as a broad classification system. This work integrates a two-layer particle swarm optimizer (TLPSO) into YOLO to reduce the contribution of SqueezeNet convolutional filters that have contributed less to human action recognition. In short, this work introduces a lightweight vision system with an optimized SqueezeNet backbone feature extraction network. Secondly, it does so without sacrificing accuracy. This is because that the high-dimensional SqueezeNet convolutional filter selection is supported by the efficient TLPSO algorithm. The proposed vision system has been used to the recognition of human behaviors from drone-mounted camera images. This study focused on two separate motions, namely walking and running. As a consequence, a total of 300 pictures were taken at various places, angles, and weather conditions, with 100 shots capturing running and 200 images capturing walking. The TLPSO technique lowered SqueezeNet’s convolutional filters by 52%, resulting in a sevenfold boost in detection speed. With an F1 score of 94.65% and an inference time of 0.061 milliseconds, the suggested system beat earlier vision systems in terms of human recognition from drone-based photographs. In addition, the performance assessment of TLPSO in comparison to other related optimizers found that TLPSO had a better convergence curve and achieved a higher fitness value. In statistical comparisons, TLPSO surpassed PSO and RLMPSO by a wide margin.

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“…The target detection algorithm based on deep learning solves the problems existing in traditional target detection algorithms, uses Convolutional neural network instead of traditional manual methods to extract image features, converts pixel information in the input image into higher-order hierarchical feature information, has stronger robustness, and is a breakthrough research in the field of target detection [12][13][14][15]. This paper aims to study a lightweight traffic scene object detection algorithm, which can be used in the vehicle embedded platform traffic object detection system.…”

Section: Introductionmentioning

confidence: 99%

Research on a multi-scale traffic scene object detection algorithm based on Light-YOLOX

Wang,

Liu

2024

Fourth International Conference on Telecommunications, Optics, and Computer Science (TOCS 2023)

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A multi-scale traffic scene object detection algorithm based on Light-YOLOX has been proposed with the aim of achieving a substantial enhancement in accuracy while maintaining a lightweight design. To accomplish this, the algorithm has undergone further refinement by introduc`ng a pyramidal attention segmentation module in the backbone network and pooling structure. This enhancement has significantly improved the algorithm ' s capacity to extract contextual information at various scales, leading to more precise and comprehensive object detection in traffic scenes. Moreover, the development of the Py-FPN feature fusion structure, achieved through the integration of multi-scale pyramidal convolution, has enabled the complete fusion of output feature layers from the backbone network. This integration has further heightened the algorithm's detection effectiveness, ensuring a more robust and accurate detection of objects within traffic scenes. Through experimental evaluations on both the KITTI dataset and Cityscapes dataset, it has been demonstrated that the proposed multi-scale traffic scene object detection method delivers a considerable improvement in accuracy and comprehensive performance, despite having a minimal number of parameters and operations. These findings underscore the algorithm ' s potential applicability and effectiveness in the practical field of traffic scene object detection, illustrating its suitability for real-world deployment and utilization.

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Bi-path Combination YOLO for Real-time Few-shot Object Detection

Cited by 13 publications

References 38 publications

Computer Vision-Assisted Object Detection and Handling Framework for Robotic Arm Design Using YOLOV5

Computer Vision-Assisted Object Detection and Handling Framework for Robotic Arm Design Using YOLOV5

Optimized deep learning vision system for human action recognition from drone images

Research on a multi-scale traffic scene object detection algorithm based on Light-YOLOX

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