YOLOMask, an Instance Segmentation Algorithm Based on Complementary Fusion Network

Jiang, Hua; Hao, Tonglin; Zeng, Liangcai; Yu, Gui

doi:10.3390/math9151766

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…where N denotes the number of all points inside the point cloud, and V denotes the maximum volume of the external voxel grid of the point cloud. The definition of V is shown in Formula (11).…”

Section: Target Point Cloud Segmentation and Model Point Cloud Genera...mentioning

confidence: 99%

“…By substituting Formulas (10) and (11) into Formula (9), we can obtain the size of L (see Formula ( 13)).…”

Section: Target Point Cloud Segmentation and Model Point Cloud Genera...mentioning

confidence: 99%

“…Wang et al [10] introduced the concept of instance type in the Solo network, which classifies each pixel of an object according to its position and size, thereby transforming the regression problem of the prediction of position into a classification problem. Jiang et al [11] proposed an instance segmentation algorithm based on a complementary fusion network, which can accurately obtain the type and location of each instance and has a good real-time performance.…”

Section: Introductionmentioning

confidence: 99%

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3D Pose Recognition of Small Special-Shaped Sheet Metal with Multi-Objective Overlapping

et al. 2023

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This paper addresses the challenging task of determining the position and posture of small-scale thin metal parts with multi-objective overlapping. To tackle this problem, we propose a method that utilizes instance segmentation and a three-dimensional (3D) point cloud for recognizing the posture of thin special-shaped metal parts. We investigate the process of obtaining a single target point cloud by aligning the target mask with the depth map. Additionally, we explore a pose estimation method that involves registering the target point cloud with the model point cloud, designing a registration algorithm that combines the sample consensus initial alignment algorithm (SAC-IA) for coarse registration and the iterative closest point (ICP) algorithm for fine registration. The experimental results demonstrate the effectiveness of our approach. The average accuracy of the instance segmentation models, utilizing ResNet50 + FPN and ResNet101 + FPN as backbone networks, exceeds 97%. The time consumption of the ResNet50 + FPN model is reduced by 50%. Furthermore, the registration algorithm, which combines the SAC-IA and ICP, achieves a lower average consumption time while satisfying the requirements for the manufacturing of new energy batteries.

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Section: Target Point Cloud Segmentation and Model Point Cloud Genera...mentioning

confidence: 99%

“…By substituting Formulas (10) and (11) into Formula (9), we can obtain the size of L (see Formula ( 13)).…”

Section: Target Point Cloud Segmentation and Model Point Cloud Genera...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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3D Pose Recognition of Small Special-Shaped Sheet Metal with Multi-Objective Overlapping

et al. 2023

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“…For example, Mask RCNN [ 6 ] extends Faster R-CNN by adding a branch for predicting an object mask, in parallel with the existing branch for bounding box recognition. Hua et al [ 7 ] also proposed instance segmentation by combining the function of semantic segmentation and object detection.…”

Section: Introductionmentioning

confidence: 99%

Contextual Detection of Pedestrians and Vehicles in Orthophotography by Fusion of Deep Learning Algorithms

Ansarnia

Tisserand

Schweitzer

et al. 2022

Sensors

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In the context of smart cities, monitoring pedestrian and vehicle movements is essential to recognize abnormal events and prevent accidents. The proposed method in this work focuses on analyzing video streams captured from a vertically installed camera, and performing contextual road user detection. The final detection is based on the fusion of the outputs of three different convolutional neural networks. We are simultaneously interested in detecting road users, their motion, and their location respecting the static environment. We use YOLOv4 for object detection, FC-HarDNet for background semantic segmentation, and FlowNet 2.0 for motion detection. FC-HarDNet and YOLOv4 were retrained with our orthophotographs dataset. The last step involves a data fusion module. The presented results show that the method allows one to detect road users, identify the surfaces on which they move, quantify their apparent velocity, and estimate their actual velocity.

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ASF-YOLO: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation

Kang,

Ting,

Ting

et al. 2024

Image and Vision Computing

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YOLOMask, an Instance Segmentation Algorithm Based on Complementary Fusion Network

Cited by 5 publications

References 21 publications

3D Pose Recognition of Small Special-Shaped Sheet Metal with Multi-Objective Overlapping

3D Pose Recognition of Small Special-Shaped Sheet Metal with Multi-Objective Overlapping

Contextual Detection of Pedestrians and Vehicles in Orthophotography by Fusion of Deep Learning Algorithms

ASF-YOLO: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation

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