SEAT-YOLO: A squeeze-excite and spatial attentive you only look once architecture for shadow detection

Kumar, Akhil

doi:10.1016/j.ijleo.2023.170513

Cited by 12 publications

(5 citation statements)

References 17 publications

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“…The feasibility of numerous image processing similarly computer vision tasks rely on upon the excellence of identifying significant edges. It is one of the classifications for detecting intensity cutoffs in a digital still image [16,17] .…”

Section: Edge Detection In Still Imagesmentioning

confidence: 99%

Design and analysis of shadow detection and removal scheme in real-time still images

Ali,

Karim,

Akhtar

et al. 2024

Comput. Soft. Media. Appl.

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This research’s main objective is to study and evaluate the detection and removal of undesired shadows from still images since these shadows might mask important information caused by light sources and other obstructions. A variety of methods for detecting and eliminating shadows as well as object tracking approaches based on movement estimation and identification are investigated. This includes shadow removal methods like background subtraction, which are intended to improve obstacle recognition of the source item and increase the accuracy of shadow removal from objects. When new items enter the frame, they are first distinguished from the background using a reference frame. The tracking procedure is made more difficult by the merging of the shadow with the foreground object. The approach highlights the difficulties in object detection owing to frequent occurrences of obstacles by using morphological procedures for shadow identification and removal. The proposed approach uses feature extraction is also discussed, highlighting its importance in image processing research and the use of suggested methods to get over obstacles in image sequences. The proposed method for shadow identification and removal offers a novel approach to improve image processing when dealing with still images. The purpose of this technique is to better detect and remove shadows from images, which will increase the precision of object tracking and detection. Depending on the type of images being processed, the process begins with initializing a background model, which is based on a static image background.

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Section: Edge Detection In Still Imagesmentioning

confidence: 99%

Design and analysis of shadow detection and removal scheme in real-time still images

Ali,

Karim,

Akhtar

et al. 2024

Comput. Soft. Media. Appl.

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show abstract

“…Huang et al 23 introduced an improved YOLOv3 algorithm Spatial Attention that is based on Gated Channel Transformation and adaptive up-sampling module, and mainly focus on the feature reaction of different location of the image, thereby improving the model’s detection performance of targets. The SEAT-YOLO proposed by Kumar et al 24 can detect small shadow area with high accuracy. By integrating Channel Attention and Spatial Attention, the Channel and Spatial Mixed Attention can better focus on the target with relatively less pixels and promote the accuracy of small target detection.…”

Section: Related Workmentioning

confidence: 99%

Metal surface defect detection based on improved YOLOv5

Zhou,

Lu,

et al. 2023

Sci Rep

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During the production of metal material, various complex defects may come into being on the surface, together with large amount of background texture information, causing false or missing detection in the process of small defect detection. To resolve those problems, this paper introduces a new model which combines the advantages of CSPlayer module and Global Attention Enhancement Mechanism based on the YOLOv5s model. First of all, we replace C3 module with CSPlayer module to augment the neural network model, so as to improve its flexibility and adaptability. Then, we introduce the Global Attention Mechanism (GAM) and build the generalized additive model. In the meanwhile, the attention weights of all dimensions are weighted and averaged as output to promote the detection speed and accuracy. The results of the experiment in which the GC10-DET augmented dataset is involved, show that the improved algorithm model performs better than YOLOv5s in precision, mAP@0.5 and mAP@0.5: 0.95 by 5.3%, 1.4% and 1.7% respectively, and it also has a higher reasoning speed.

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“…Because it uses a single convolutional neural network to estimate bounding boxes and class probabilities directly from complete pictures, YOLO is faster and more accurate than earlier object detection algorithms [ 40 ]. Numerous applications, including self-driving cars, pedestrian detection, and face detection, have effectively exploited YOLO for object detection [ 81 , 82 , 83 ].…”

Section: Literature Reviewmentioning

confidence: 99%

YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images

et al. 2023

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Cell counting in fluorescence microscopy is an essential task in biomedical research for analyzing cellular dynamics and studying disease progression. Traditional methods for cell counting involve manual counting or threshold-based segmentation, which are time-consuming and prone to human error. Recently, deep learning-based object detection methods have shown promising results in automating cell counting tasks. However, the existing methods mainly focus on segmentation-based techniques that require a large amount of labeled data and extensive computational resources. In this paper, we propose a novel approach to detect and count multiple-size cells in a fluorescence image slide using You Only Look Once version 5 (YOLOv5) with a feature pyramid network (FPN). Our proposed method can efficiently detect multiple cells with different sizes in a single image, eliminating the need for pixel-level segmentation. We show that our method outperforms state-of-the-art segmentation-based approaches in terms of accuracy and computational efficiency. The experimental results on publicly available datasets demonstrate that our proposed approach achieves an average precision of 0.8 and a processing time of 43.9 ms per image. Our approach addresses the research gap in the literature by providing a more efficient and accurate method for cell counting in fluorescence microscopy that requires less computational resources and labeled data.

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SEAT-YOLO: A squeeze-excite and spatial attentive you only look once architecture for shadow detection

Cited by 12 publications

References 17 publications

Design and analysis of shadow detection and removal scheme in real-time still images

Design and analysis of shadow detection and removal scheme in real-time still images

Metal surface defect detection based on improved YOLOv5

YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images

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