User-interactive salient object detection using YOLOv2, lazy snapping, and gabor filters

Srivastava, Gargi; Srivastava, Rajeev

doi:10.1007/s00138-020-01065-6

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Elucidating various versions of YOLO YOLOv1 [42] YOLO9000/ YOLOv2 [43] YOLOv3 [44] YOLOv4 [45] YOLOv5 [46] YOLO Based on Fig. 8, input is fed into Yolov5 Backbone via the focus layer.…”

Section: Tablementioning

confidence: 99%

An improved deep learning-based optimal object detection system from images

Yadav,

Jindal,

Rani

et al. 2023

Multimed Tools Appl

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Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms—You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss.

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“…Elucidating various versions of YOLO YOLOv1 [42] YOLO9000/ YOLOv2 [43] YOLOv3 [44] YOLOv4 [45] YOLOv5 [46] YOLO Based on Fig. 8, input is fed into Yolov5 Backbone via the focus layer.…”

Section: Tablementioning

confidence: 99%