Object detection method based on global feature augmentation and adaptive regression in IoT

Li, Hui; Dong, Yan; Xu, Lingwei; Zhang, Shujun; Wang, Junyin

doi:10.1007/s00521-020-05633-9

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…With the development of the manufacturing industry, intelligent manufacturing technology has gradually become the key technology to realize the knowledge, automation, and flexibility of manufacturing to achieve rapid response to the market [ 17 – 19 ]. In particular, vision-based object detection to detect objects in manufacturing scenes can effectively improve intelligent production.…”

Section: Related Workmentioning

confidence: 99%

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Chen

et al. 2022

Computational Intelligence and Neuroscience

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Vision-based object detection of PCB (printed circuit board) assembly scenes is essential in accelerating the intelligent production of electronic products. In particular, it is necessary to improve the detection accuracy as much as possible to ensure the quality of assembly products. However, the lack of object detection datasets in PCB assembly scenes is the key to restricting intellectual PCB assembly research development. As an excellent representative of the one-stage object detection model, YOLOv3 (you only look once version 3) mainly relies on placing predefined anchors on the three feature pyramid layers and realizes recognition and positioning using regression. However, the number of anchors distributed in each grid cell of different scale feature layers is usually the same. The ERF (effective receptive field) corresponding to the grid cell at different locations varies. The contradiction between the uniform distribution of fixed-size anchors and the ERF size range in different feature layers will reduce the effectiveness of object detection. Few people use ERF as a standard for assigning anchors to improve detection accuracy. To address this issue, firstly, we constructed a PCB assembly scene object detection dataset, which includes 21 classes of detection objects in three scenes before assembly, during assembly, and after assembly. Secondly, we performed a refined ERF analysis on each grid of the three output layers of YOLOv3, determined the ERF range of each layer, and proposed an anchor allocation rule based on the ERF. Finally, for the small and difficult-to-detect TH (through-holes), we increased the context information and designed improved-ASPP (Atrous spatial pyramid pooling) and channel attention joint module. Through a series of experiments on the object detection dataset of the PCB assembly scene, we found that under the framework of YOLOv3, anchor allocation based on ERF can increase mAP (mean average precision) from 79.32% to 89.86%. At the same time, our proposed method is superior to Faster R-CNN (region convolution neural network), SSD (single shot multibox detector), and YOLOv4 (you only look once version 4) in the balance of high detection accuracy and low computational complexity.

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Section: Related Workmentioning

confidence: 99%

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Chen

et al. 2022

Computational Intelligence and Neuroscience

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“…Since the value range of R, G, B is 0~255, the gray level is only 256. That is, grayscale images can only show 256 grayscales [19,20]. The image gray processing method used in this paper is weighted average method.…”

Section: Image Preprocessingmentioning

confidence: 99%

Real-Time Monitoring of College Sports Dance Competition Scenes Using Deep Learning Algorithms

Yang

Shan

2022

Wireless Communications and Mobile Computing

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In order to improve the real-time detection effect, therefore, a research on real-time scene detection of sports dance competition based on deep learning is proposed. The collected scene image is grayed by using the weighted average method, and the best image interpolation is calculated by using the deep learning method, so as to realize the smooth processing of sawtooth and mosaic information generated by panoramic mapping. After selecting the cube model, the processed scene information is projected to the visual plane to construct the panorama of the competition scene. Finally, combined with the three-frame difference, the changes between adjacent image frames are calculated to obtain the moving target. The test results show that the motion detection accuracy of professional dancers can reach more than 75.0% and that of amateur dancer can reach more than 64.2%.

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“…Poor lighting conditions will also affect the RGB-D camera's extraction of target depth information, and then affect the generation and transformation of 3D point cloud data [36][37][38]. In addition to external environment factors, the target to be detected also has a great impact on object detection and instance segmentation [39][40][41]. In actual logistics transfer center scenes, there are a large number of packages with different shapes, colors and materials stacked in a disorderly manner.…”

Section: Introductionmentioning

confidence: 99%

Visual Sorting of Express Packages Based on the Multi-Dimensional Fusion Method under Complex Logistics Sorting

Ren

Liu

et al. 2023

Entropy

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Visual sorting of express packages is faced with many problems such as the various types, complex status, and the changeable detection environment, resulting in low sorting efficiency. In order to improve the sorting efficiency of packages under complex logistics sorting, a multi-dimensional fusion method (MDFM) for visual sorting in actual complex scenes is proposed. In MDFM, the Mask R-CNN is designed and applied to detect and recognize different kinds of express packages in complex scenes. Combined with the boundary information of 2D instance segmentation from Mask R-CNN, the 3D point cloud data of grasping surface is accurately filtered and fitted to determining the optimal grasping position and sorting vector. The images of box, bag, and envelope, which are the most common types of express packages in logistics transportation, are collected and the dataset is made. The experiments with Mask R-CNN and robot sorting were carried out. The results show that Mask R-CNN achieves better results in object detection and instance segmentation on the express packages, and the robot sorting success rate by the MDFM reaches 97.2%, improving 2.9, 7.5, and 8.0 percentage points, respectively, compared to baseline methods. The MDFM is suitable for complex and diverse actual logistics sorting scenes, and improves the efficiency of logistics sorting, which has great application value.

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Object detection method based on global feature augmentation and adaptive regression in IoT

Cited by 5 publications

References 23 publications

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Real-Time Monitoring of College Sports Dance Competition Scenes Using Deep Learning Algorithms

Visual Sorting of Express Packages Based on the Multi-Dimensional Fusion Method under Complex Logistics Sorting

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