Towards the steel plate defect detection: Multidimensional feature information extraction and fusion

Hao, Zhiqiang; Wang, Zhigang; Bai, Dongxu; Zhou, Shiyang

doi:10.1002/cpe.6384

Cited by 20 publications

(17 citation statements)

References 57 publications

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“…The results show that the detection performance of the improved model is higher than Faster RCNN, YOLOv3, and SSD models. [77][78][79][80][81] This is due to the improved model designing a more efficient basic network, and the use of focal loss to improve the problem of sample imbalance.…”

Section: Model Trainingmentioning

confidence: 99%

Improved single shot multibox detector target detection method based on deep feature fusion

Bai

Sun

Tao

et al. 2021

Concurrency and Computation

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The feature layers of different layers in the single shot multibox detector (SSD) are independently used as the input of the classification network, so it is easy to detect the same object. This article proposes an improved SSD model based on deep feature fusion. In the SSD algorithm, the deep feature fusion between the target detection layer and its adjacent feature layer is used, including convolution kernels and pooling kernels of different sizes, down-sampling of low-level features and up-sampling of deconvolution of high-level features. The network is improved by combining the target frame recommendation strategy in the SSD algorithm and the frame regression algorithm. The experimental results show that the improved SSD algorithm improves the detection accuracy and detection rate of the target, and the effect is more obvious for the relatively small-scale target.

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Section: Model Trainingmentioning

confidence: 99%

Improved single shot multibox detector target detection method based on deep feature fusion

Bai

Sun

Tao

et al. 2021

Concurrency and Computation

Self Cite

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show abstract

“…The existing intelligent algorithms are artificial neural networks, adaptive neuro-fuzzy inference systems, and genetic algorithms, particle swarm search algorithms, etc. ( EI-Sherbiny et al, 2018 ; Huang et al, 2021 ; Tao et al, 2021 ; Hao et al, 2021a ).…”

Section: Related Workmentioning

confidence: 99%

A Tandem Robotic Arm Inverse Kinematic Solution Based on an Improved Particle Swarm Algorithm

Zhao

Liu

et al. 2022

Front. Bioeng. Biotechnol.

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The analysis of robot inverse kinematic solutions is the basis of robot control and path planning, and is of great importance for research. Due to the limitations of the analytical and geometric methods, intelligent algorithms are more advantageous because they can obtain approximate solutions directly from the robot’s positive kinematic equations, saving a large number of computational steps. Particle Swarm Algorithm (PSO), as one of the intelligent algorithms, is widely used due to its simple principle and excellent performance. In this paper, we propose an improved particle swarm algorithm for robot inverse kinematics solving. Since the setting of weights affects the global and local search ability of the algorithm, this paper proposes an adaptive weight adjustment strategy for improving the search ability. Considering the running time of the algorithm, this paper proposes a condition setting based on the limit joints, and introduces the position coefficient k in the velocity factor. Meanwhile, an exponential product form modeling method (POE) based on spinor theory is chosen. Compared with the traditional DH modeling method, the spinor approach describes the motion of a rigid body as a whole and avoids the singularities that arise when described by a local coordinate system. In order to illustrate the advantages of the algorithm in terms of accuracy, time, convergence and adaptability, three experiments were conducted with a general six-degree-of-freedom industrial robotic arm, a PUMA560 robotic arm and a seven-degree-of-freedom robotic arm as the research objects. In all three experiments, the parameters of the robot arm, the range of joint angles, and the initial attitude and position of the end-effector of the robot arm are given, and the attitude and position of the impact point of the end-effector are set to verify whether the joint angles found by the algorithm can reach the specified positions. In Experiments 2 and 3, the algorithm proposed in this paper is compared with the traditional particle swarm algorithm (PSO) and quantum particle swarm algorithm (QPSO) in terms of position and direction solving accuracy, operation time, and algorithm convergence. The results show that compared with the other two algorithms, the algorithm proposed in this paper can ensure higher position accuracy and orientation accuracy of the robotic arm end-effector. the position error of the algorithm proposed in this paper is 0 and the maximum orientation error is 1.29 × 10–8. while the minimum position error of the other two algorithms is −1.64 × 10–5 and the minimum orientation error is −4.03 × 10–6. In terms of operation time, the proposed algorithm in this paper has shorter operation time compared with the other two algorithms. In the last two experiments, the computing time of the proposed algorithm is 0.31851 and 0.30004s respectively, while the shortest computing time of the other two algorithms is 0.33359 and 0.30521s respectively. In terms of algorithm convergence, the proposed algorithm can achieve faster and more stable convergence than the other two algorithms. After changing the experimental subjects, the proposed algorithm still maintains its advantages in terms of accuracy, time and convergence, which indicates that the proposed algorithm is more applicable and has certain potential in solving the multi-arm inverse kinematics solution. This paper provides a new way of thinking for solving the multi-arm inverse kinematics solution problem.

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“…However, due to the influence of light, weather and imaging equipment, the captured images are often dark, noisy, poorly contrasted and partially obliterated in detail in real life ( Sun et al, 2020a ; Tan et al, 2020 ; Wang et al, 2020 ). This kind of image makes the area of interest difficult to identify, thus reducing the quality of image and the visual effect of the human eyes ( Jiang et al, 2019b ; Hu et al, 2019 ), and also causes great inconvenience for the extraction and analysis of image information, generating considerable difficulty for computers and other vision devices to carry out normal target detection and recognition ( Su and Jung, 2018 ; Sun et al, 2020b ; Cheng et al, 2020 ; Luo et al, 2020 ; Hao et al, 2021b ). Therefore, it is necessary to enhance the low-light images through image enhancement technology ( Jiang et al, 2019c ; Sun et al, 2020c ), so as to highlight the detailed features of the original images, improve contrast, reduce noise, make the original blurred and low recognition images clear, improve the recognition and interpretation of images comparatively, and satisfy the requirements of certain specific occasions ( Tao et al, 2017 ; Ma et al, 2020 ; Jiang et al, 2021a ; Tao et al, 2021 ; Liu et al, 2022b ).…”

Section: Introductionmentioning

confidence: 99%

Low-Illumination Image Enhancement Algorithm Based on Improved Multi-Scale Retinex and ABC Algorithm Optimization

Sun

Zhao

et al. 2022

Front. Bioeng. Biotechnol.

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In order to solve the problems of poor image quality, loss of detail information and excessive brightness enhancement during image enhancement in low light environment, we propose a low-light image enhancement algorithm based on improved multi-scale Retinex and Artificial Bee Colony (ABC) algorithm optimization in this paper. First of all, the algorithm makes two copies of the original image, afterwards, the irradiation component of the original image is obtained by used the structure extraction from texture via relative total variation for the first image, and combines it with the multi-scale Retinex algorithm to obtain the reflection component of the original image, which are simultaneously enhanced using histogram equalization, bilateral gamma function correction and bilateral filtering. In the next part, the second image is enhanced by histogram equalization and edge-preserving with Weighted Guided Image Filtering (WGIF). Finally, the weight-optimized image fusion is performed by ABC algorithm. The mean values of Information Entropy (IE), Average Gradient (AG) and Standard Deviation (SD) of the enhanced images are respectively 7.7878, 7.5560 and 67.0154, and the improvement compared to original image is respectively 2.4916, 5.8599 and 52.7553. The results of experiment show that the algorithm proposed in this paper improves the light loss problem in the image enhancement process, enhances the image sharpness, highlights the image details, restores the color of the image, and also reduces image noise with good edge preservation which enables a better visual perception of the image.

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Towards the steel plate defect detection: Multidimensional feature information extraction and fusion

Cited by 20 publications

References 57 publications

Improved single shot multibox detector target detection method based on deep feature fusion

Improved single shot multibox detector target detection method based on deep feature fusion

A Tandem Robotic Arm Inverse Kinematic Solution Based on an Improved Particle Swarm Algorithm

Low-Illumination Image Enhancement Algorithm Based on Improved Multi-Scale Retinex and ABC Algorithm Optimization

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