Object Detection Method for Grasping Robot Based on Improved YOLOv5

Song, Qisong; Li, Shaobo; Bai, Qiang; Yang, Jing; Zhang, Xingxing; Li, Zhiang; Duan, Zhongjing

doi:10.3390/mi12111273

Cited by 70 publications

(31 citation statements)

References 38 publications

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“…The head module is responsible for generating target prediction boxes to determine the category, coordinates, and confidence level of the detected object ( Wen et al., 2021 ). Its network contains four network structures of different sizes (YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x), thus allowing the user to choose the appropriate model according to their actual needs ( Song et al., 2021 ). Since this research mainly considers the accuracy problem when selecting the recognition algorithm, and does not require high real-time requirement of the algorithm, the YOLOv5x network with the deepest network depth and the widest feature map width is selected.…”

Section: Methodsmentioning

confidence: 99%

Recognition of terminal buds of densely-planted Chinese fir seedlings using improved YOLOv5 by integrating attention mechanism

Guo

Wei

et al. 2022

Front. Plant Sci.

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Accurate and timely information on the number of densely-planted Chinese fir seedlings is essential for their scientific cultivation and intelligent management. However, in the later stage of cultivation, the overlapping of lateral branches among individuals is too severe to identify the entire individual in the UAV image. At the same time, in the high-density planting nursery, the terminal bud of each seedling has a distinctive characteristic of growing upward, which can be used as an identification feature. Still, due to the small size and dense distribution of the terminal buds, the existing recognition algorithm will have a significant error. Therefore, in this study, we proposed a model based on the improved network structure of the latest YOLOv5 algorithm for identifying the terminal bud of Chinese fir seedlings. Firstly, the micro-scale prediction head was added to the original prediction head to enhance the model’s ability to perceive small-sized terminal buds. Secondly, a multi-attention mechanism module composed of Convolutional Block Attention Module (CBAM) and Efficient Channel Attention (ECA) was integrated into the neck of the network to enhance further the model’s ability to focus on key target objects in complex backgrounds. Finally, the methods including data augmentation, Test Time Augmentation (TTA) and Weighted Boxes Fusion (WBF) were used to improve the robustness and generalization of the model for the identification of terminal buds in different growth states. The results showed that, compared with the standard version of YOLOv5, the recognition accuracy of the improved YOLOv5 was significantly increased, with a precision of 95.55%, a recall of 95.84%, an F1-Score of 96.54%, and an mAP of 94.63%. Under the same experimental conditions, compared with other current mainstream algorithms (YOLOv3, Faster R-CNN, and PP-YOLO), the average precision and F1-Score of the improved YOLOv5 also increased by 9.51-28.19 percentage points and 15.92-32.94 percentage points, respectively. Overall, The improved YOLOv5 algorithm integrated with the attention network can accurately identify the terminal buds of densely-planted Chinese fir seedlings in UAV images and provide technical support for large-scale and automated counting and precision cultivation of Chinese fir seedlings.

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Section: Methodsmentioning

confidence: 99%

Recognition of terminal buds of densely-planted Chinese fir seedlings using improved YOLOv5 by integrating attention mechanism

Guo

Wei

et al. 2022

Front. Plant Sci.

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“…The CBL is a simple convolution module. In the hidden layers of YOLO v5, only the leaky-relu activation function (CBL module) is used [44].…”

Section: Network Architecture Of Yolov5mentioning

confidence: 99%

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

Kisaezehra¹,

Farooq²,

Bhutto³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety (OHS) is of prime importance. Like in other developing countries, this industry pays very little, rather negligible attention to OHS practices in Pakistan, resulting in the occurrence of a wide variety of accidents, mishaps, and near-misses every year. One of the major causes of such mishaps is the non-wearing of safety helmets (hard hats) at construction sites where falling objects from a height are unavoidable. In most cases, this leads to serious brain injuries in people present at the site in general and the workers in particular. It is one of the leading causes of human fatalities at construction sites. In the United States, the Occupational Safety and Health Administration (OSHA) requires construction companies through safety laws to ensure the use of well-defined personal protective equipment (PPE). It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible. However, such monitoring through technological aids or automated tools is very much possible. The present study describes a systematic strategy based on deep learning (DL) models built on the You-Only-Look-Once (YOLOV5) architecture that could be used for monitoring workers' hard hats in real-time. It can indicate whether a worker is wearing a hat or not. The proposed system uses five different models of the YOLOV5, namely YOLOV5n, YOLOv5s, YOLOv5 m, YOLOv5l, and YOLOv5x for object detection with the support of PyTorch, involving 7063 images. The results of the study show that among the DL models, the YOLOV5x has a high performance of 95.8% in terms of the mAP, while the YOLOV5n has the fastest detection speed of 70.4 frames per second (FPS). The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.

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“…This has led to the pursuit of alternative solutions based on more low-cost 3D vision cameras, investing in the research and the improvements of the machine learning algorithms. One such solution is proposed in [12], where the authors propose an object detection method based on the YOLOv5 algorithm, which can perform accurate positioning and recognition of objects to be grasped by an arm robot with an Intel RealSense D415 camera in an eye-to-hand configuration.…”

Section: Related Workmentioning

confidence: 99%

Bin-Picking Solution for Randomly Placed Automotive Connectors Based on Machine Learning Techniques

et al. 2022

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This paper presents the development of a bin-picking solution based on low-cost vision systems for the manipulation of automotive electrical connectors using machine learning techniques. The automotive sector has always been in a state of constant growth and change, which also implies constant challenges in the wire harnesses sector, and the emerging growth of electric cars is proof of this and represents a challenge for the industry. Traditionally, this sector is based on strong human work manufacturing and the need arises to make the digital transition, supported in the context of Industry 4.0, allowing the automation of processes and freeing operators for other activities with more added value. Depending on the car model and its feature packs, a connector can interface with a different number of wires, but the connector holes are the same. Holes not connected with wires need to be sealed, mainly to guarantee the tightness of the cable. Seals are inserted manually or, more recently, through robotic stations. Due to the huge variety of references and connector configurations, layout errors sometimes occur during seal insertion due to changed references or problems with the seal insertion machine. Consequently, faulty connectors are dumped into boxes, piling up different types of references. These connectors are not trash and need to be reused. This article proposes a bin-picking solution for classification, selection and separation, using a two-finger gripper, of these connectors for reuse in a new operation of removal and insertion of seals. Connectors are identified through a 3D vision system, consisting of an Intel RealSense camera for object depth information and the YOLOv5 algorithm for object classification. The advantage of this approach over other solutions is the ability to accurately detect and grasp small objects through a low-cost 3D camera even when the image resolution is low, benefiting from the power of machine learning algorithms.

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Object Detection Method for Grasping Robot Based on Improved YOLOv5

Cited by 70 publications

References 38 publications

Recognition of terminal buds of densely-planted Chinese fir seedlings using improved YOLOv5 by integrating attention mechanism

Recognition of terminal buds of densely-planted Chinese fir seedlings using improved YOLOv5 by integrating attention mechanism

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

Bin-Picking Solution for Randomly Placed Automotive Connectors Based on Machine Learning Techniques

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