A hybrid tiny YOLO v4-SPP module based improved face mask detection vision system

Kumar, Akhil; Kalia, Arvind; Sharma, Akashdeep; Kaushal, Manisha

doi:10.1007/s12652-021-03541-x

Cited by 42 publications

(22 citation statements)

References 28 publications

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“…Something else to note is that there have been various other papers that aimed at improving the overall architecture of YOLOv4 itself, with various different approaches [15,18,21,23].…”

Section: Discussionmentioning

confidence: 99%

“…Thanks to the region proposal, the detections tend to be sparse, and the algorithm can identify the objects better. On the other hand, if frequent detections are necessary, then a single-stage detector [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] is better since the two-stage ones tend to be rather slow. They sacrifice accuracy for detection frequency by skipping the region proposal stage and detecting the objects directly on the feature maps.…”

Section: Introductionmentioning

confidence: 99%

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Fast and Robust People Detection in RGB Images

2022

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People detection in images has many uses today, ranging from face detection algorithms used by social networks to help the users tag other people, to surveillance systems that can create a statistic of the population density in an area, or identify a suspect, or even in the automotive industry as part of the Pedestrian Crash Avoidance Mitigation (PCAM) system. This work focuses on creating a fast and reliable object detection algorithm that will be trained on scenes that depict people in an indoor environment, starting from an existing state-of-the-art approach. The proposed method improves upon the You Only Look Once version 4 (YOLOv4) network by adding a region of interest classification and regression branch such as Faster R-CNN’s head. The candidate bounding boxes proposed by YOLOv4 are ranked based on their confidence score, the best candidates being kept and sent as input to the Faster Region-Based Convolutional Neural Network (R-CNN) head. To keep only the best detections, non-maximum suppression is applied to all proposals. This decreases the number of false-positive candidate bounding boxes, the low-confidence detections of the regression and classification branch being eliminated by the detections of YOLOv4 and vice versa in the non-maximum suppression step. This method can be used as the object detection algorithm in an image-based people tracking system, namely Tracktor, having a higher inference speed than Faster R-CNN. Our proposed method manages to achieve an overall accuracy of 95% and an inference time of 22 ms.

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“…Something else to note is that there have been various other papers that aimed at improving the overall architecture of YOLOv4 itself, with various different approaches [15,18,21,23].…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast and Robust People Detection in RGB Images

2022

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“…The tiny YOLO v4 algorithm in recent times has shown fascinating results in general object category detection on the MS COCO dataset [26] . Few researchers by proposing new variants of tiny YOLO v4 for face mask detection have scaled up the performance in terms of speed and detection [27] . However, the issue of inaccurate detection in distant images and the ability to detect small objects still surface in the published work.…”

Section: Methodsmentioning

confidence: 99%

ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic

Kumar

Kalia

2022

Optik

Self Cite

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“…However, the mean average precision (mAP) of YOLO-V4 Tiny (mAP 22%) was inferior to YOLO-V4 (mAP 43.5%) on the MS COCO dataset, although it was faster than YOLO-V4 in detection speed (27). The YOLO-V4 Tiny had been shown in practice to be better at detecting independent objects, but struggled with detecting small objects and overlapping targets (28). For identification of CACs, high accuracy and quick response are both required clinically.…”

Section: Replacing the Backbonementioning

confidence: 99%

An efficient fluorescence in situ hybridization (FISH)-based circulating genetically abnormal cells (CACs) identification method based on Multi-scale MobileNet-YOLO-V4

Xu¹,

Zhang²,

Fan³

et al. 2022

Quant Imaging Med Surg

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Background: Circulating tumor cells (CTCs) acting as "liquid biopsy" of cancer are cells that have been shed from the primary tumor, which cause the development of a secondary tumor in a distant organ site, leading to cancer metastasis. Recent research suggests that CTCs with abnormalities in gene copy numbers in mononuclear cell-enriched peripheral blood samples, namely circulating genetically abnormal cells (CACs), could be used as a non-invasive decision tool to detect patients with benign pulmonary nodules. Such cells are identified by counting the fluorescence signals of fluorescence in situ hybridization (FISH). However, owing to the rarity of CACs in the blood, identification of CACs using this technique is time-consuming and is a drawback of this method.Methods: This study has proposed an efficient and automatic FISH-based CACs identification approach which is based on a combination of the high accuracy of You Only Look Once (YOLO)-V4 and the lightweight and rapidness of MobileNet-V3. The backbone of YOLO-V4 was replaced with MobileNet-V3 to improve the detection efficiency and prevent overfitting, and the architecture of YOLO-V4 was optimized by utilizing a new feature map with a larger scale to enable the enhanced detection ability for small targets.Results: We trained and tested the proposed model using a dataset containing more than 7,000 cells based on five-fold cross-validation. All the images in the dataset were 2,448×2,048 (pixels) in size. The number of cells in each image was >70. The accuracy of four-color fluorescence signals detection for our proposed model were all approximately 98%, and the mean average precision (mAP) were close to 100%. The final outcome of the developed method was the type of cells, i.e., normal cells, CACs, gaining cells or deletion cells. The method had a CACs identification accuracy of 93.86% (similar to an expert pathologist), and a detection speed that was about 500 times greater than that of a pathologist. Conclusions:The developed method could greatly increase the review accuracy, enhance the efficiency of reviewers, and reduce the review turnaround time during CACs identification.

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A hybrid tiny YOLO v4-SPP module based improved face mask detection vision system

Cited by 42 publications

References 28 publications

Fast and Robust People Detection in RGB Images

Fast and Robust People Detection in RGB Images

ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic

An efficient fluorescence in situ hybridization (FISH)-based circulating genetically abnormal cells (CACs) identification method based on Multi-scale MobileNet-YOLO-V4

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