Align-Yolact: a one-stage semantic segmentation network for real-time object detection

Shaodan, Lin; Zhu, Kexin; Feng, Chen; Chen, Zhide

doi:10.1007/s12652-021-03340-4

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Finally, in instance segmentation accuracy improvement [39], YOLACT was designed with multiple feature extraction and FPN to enhance instance segmentation. The results showed that ResNetSt50 has 49.66 mAP higher than other feature extraction methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Instance Segmentation Evaluation For Traffic Signs

Shi Heng Siow,

Abu Ubaidah Shamsudin,

Zubair Adil Soomro

et al. 2023

ARASET

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This research paper focuses on developing a traffic sign recognition system based on the You Only Look At Coefficients (YOLACT) model, a one-stage instance segmentation model that offers high performance in terms of accuracy and reliability. However, the performance of YOLACT is influenced by various conditions such as day/night and different angles of objects. Therefore, this study aims to evaluate the impact of different angles and environments on the performance of the system. The paper discusses the framework, backbone structure, prototype generation branch, mask coefficient, and mask assembly used in the system. ResNet-101 and ResNet-50 were used as the backbone structure to extract feature maps of objects in the input image. The prototype generation branch generates prototype masks using fully convolutional networks (FCN), and the mask coefficient branch generates the Mask assembly using the sigmoid nonlinearity. Two models, YOLACT and Mask-RCNN, were evaluated in terms of mean precision (mAP) and frames per second (FPS) with the front view dataset. The results show that YOLACT outperforms Mask-RCNN in terms of accuracy and speed. For an image resolution of 550x550, YOLACT with Resnet-101 was considered the best model in this article since it achieves over 80% precision, recall, specificity, and accuracy in various conditions such as day, night, left and right, and forward-looking angles.

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Section: Literature Reviewmentioning

confidence: 99%

Instance Segmentation Evaluation For Traffic Signs

Shi Heng Siow,

Abu Ubaidah Shamsudin,

Zubair Adil Soomro

et al. 2023

ARASET

View full text Add to dashboard Cite

show abstract

“…In the two-stage model, the re-pooling operations are logically serial to map features by the bounding box, and it is an arduous task to speed up. The You Only Look At CoefficienTs (YOLACT) is a one-stage instance segmentation model based on the one-stage object detector [20], [21]. The YOLACT algorithm abandons the implicit feature location step and separates the instance segmentation tasks into two parallel subtasks.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Instance Segmentation of Metal Screw Defects Based on Deep Learning Approach

Chen

Tsao

Lai

et al. 2022

Measurement Science Review

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In general, manual methods are often used to inspect defects in the production of metal screws. As deep learning shines in the field of visual detection, this study employs the You Only Look At CoefficienTs (YOLACT) algorithm to detect the surface defects of the metal screw heads. The raw images with different defects are collected by an automated microscopic camera scanning system to build the training and validation datasets. The experimental results demonstrate that the trained YOLACT is sufficient to achieve a mean average accuracy of 92.8 % with low missing and false rates. The processing speed of the trained YOLACT reaches 30 frames per second. Compared with other segmentation methods, the proposed model provides excellent performance in both segmentation and detection accuracy. Our efficient deep learning-based system may support the advancement of non-contact defect assessment methods for quality control of the screw manufacture.

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Early Detection of Rice Blast Using a Semi-Supervised Contrastive Unpaired Translation Iterative Network Based on UAV Images

Lin,

Li,

Huang

et al. 2023

Plants

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Rice blast has caused major production losses in rice, and thus the early detection of rice blast plays a crucial role in global food security. In this study, a semi-supervised contrastive unpaired translation iterative network is specifically designed based on unmanned aerial vehicle (UAV) images for rice blast detection. It incorporates multiple critic contrastive unpaired translation networks to generate fake images with different disease levels through an iterative process of data augmentation. These generated fake images, along with real images, are then used to establish a detection network called RiceBlastYolo. Notably, the RiceBlastYolo model integrates an improved fpn and a general soft labeling approach. The results show that the detection precision of RiceBlastYolo is 99.51% under intersection over union (IOU0.5) conditions and the average precision is 98.75% under IOU0.5–0.9 conditions. The precision and recall rates are respectively 98.23% and 99.99%, which are higher than those of common detection models (YOLO, YOLACT, YOLACT++, Mask R-CNN, and Faster R-CNN). Additionally, external data also verified the ability of the model. The findings demonstrate that our proposed model can accurately identify rice blast under field-scale conditions.

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Align-Yolact: a one-stage semantic segmentation network for real-time object detection

Cited by 3 publications

References 17 publications

Instance Segmentation Evaluation For Traffic Signs

Instance Segmentation Evaluation For Traffic Signs

Real-Time Instance Segmentation of Metal Screw Defects Based on Deep Learning Approach

Early Detection of Rice Blast Using a Semi-Supervised Contrastive Unpaired Translation Iterative Network Based on UAV Images

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