Correction to: CGAN: closure-guided attention network for salient object detection

Das, Dibyendu; Shit, Sahadeb; Ray, Dip Narayan; Majumder, S.

doi:10.1007/s00371-022-02680-2

Cited by 4 publications

(1 citation statement)

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“…In recent years, more and more researchers begin to pay attention to the importance of computer vision [4] and deep learning [5] technology in this field. At present, target detection algorithms based on deep learning are mainly divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

Indoor object recognition based on YOLOv5 with EIOU loss function

Weimin,

Hangong,

Yuting

et al. 2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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At present, large-scale object classification and recognition tasks inevitably encounter problems of training efficiency and model accuracy. The solution of this problem highly depends on the definition of loss function. A better loss function can get a more accurate model with fewer epoches, which undoubtedly helps to save computility and time cost and improve the security of the model. Nowadays, the task of indoor object recognition plays an important role in the fields of computer vision aided blind people, home robots and so on. However, the task of indoor object recognition is plagued by the above problems due to its huge category of recognition objects. Starting with the task of indoor object recognition, this paper innovatively uses EIOU loss function and YOLOv5 deep learning convolutional neural network in this field, which improves training efficiency and recognition accuracy. Some original bounding box regression loss functions of YOLO series (such as GIOU, DIOU, CIOU) have defects in that the prediction box coincides with the center point of the truth value box, and the horizontal vertical ratio is the same. The paper uses the EIOU loss function to solve this problem. This paper focuses on the network structure of YOLOv5, the defects of YOLOv5's native loss function, the calculation method and advantages of EIOU loss function, and compares the performance gap between EIOU and CIOU. Finally, the EIOU loss function is used to complete the indoor article identification task.

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Section: Introductionmentioning

confidence: 99%