Object Detection and Pose Estimation Approaches for Soldering Danger Detection

Yasunaga, Tomoya; Oda, Tetsuya; Saito, Nobuki; Hirata, Aoto; Toyoshima, Kyohei; Katayama, Kengo

doi:10.1109/gcce53005.2021.9621849

Cited by 11 publications

(1 citation statement)

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“…Using image processing and machine learning for object detection or fault detection, an application was recently developed known as HPD, which helps to detect the fault of an object [10]. Some other specific object detection applications include face detection and recognition [11], text detection [12], pose detection [13], people counting [14], and wind tracking algorithms [15]. This paper is organized into 5 sections.…”

Section: Introductionmentioning

confidence: 99%

The Object Detection, Perspective and Obstacles In Robotic: A Review

Khan

Moshayedi

et al. 2022

EAI Endorsed Trans AI Robotics

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A few years back, when the image processing hardware and software were created, it was limited, and most of the time, object detection would fail., But as with time, the advancement in technology has significantly changed the scenario. A lot of researchers worked in this field to carry out a solution through which they can detect objects in any field, especially in the robotic domain [1]. In today's world, with so much research in the field of deep learning, it is very easy to identify and detect any object using computer vision. This paper focuses on the various deep learning technologies and algorithms through which object detection can be done. A new and advanced deep learning technology known as salient object detection has been discussed. Also, the 3D object detection and the end-to-end approach for object detection are discussed. The existing methods of deep learning through which object detection can be done. The applications in which object detection is being used and the importance of object detection. It also reports; what the predecessors have done, what problems have been solved by them, how they solved these problems, the characteristics of the predecessors' methods and their future work.

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