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- Title
Exploring the efficacy and comparative analysis of one-stage object detectors for computer vision: a review.
- Authors
Mustapha, Ahmad Abubakar; Yoosuf, Mohamed Sirajudeen
- Abstract
One-stage object detection is a technique that uses a single deep neural network to detect objects in an image or video. This method trains the network from start to end to recognize objects and determine their location in a single forward pass. This method's speed and efficiency are its key benefits since they do away with the requirement for many stages or proposals and shorten calculation times. By limiting our survey to only recent one-stage object detection models, the paper was able to fulfil our objectives. In this paper, various object detection models are explored, and comparative analysis has been done, it is concluded that the main difficulty in one-stage object detection is striking a balance between accuracy and speed, as the network must make accurate predictions for numerous objects in real-time while still accurately detecting objects.
- Subjects
ARTIFICIAL neural networks; DETECTORS; COMPARATIVE studies; COMPUTER vision; OBJECT recognition (Computer vision)
- Publication
Multimedia Tools & Applications, 2024, Vol 83, Issue 20, p59143
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-023-17751-2