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Object Detection: Training From Scratch
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DOI：10.1109/ACCESS.2020.3018131.png)
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摘要
En 中文
The development of deep neural networks has driven the development of computer vision. Deep neural networks play an important role in object detection. To improve network performance, before using neural networks for object detection, they are commonly pre-trained on the data set and fine-tuned to their object detection tasks. Pre-training is not always helpful in object detection tasks, so studies have been performed on training neural networks from scratch. By consulting many relevant studies,we performed a systematic analysis of training networks from scratch for object detection. Our article is divided into the following three parts: (i) the reasons for which target detection requires training from scratch, (ii) mainstream networks that can be trained from scratch, and (iii) the criteria for training from scratch. Finally, we summarize some research directions relevant to this topic.
Keyword:
Object detection
Training
Convolution
Proposals
Feature extraction
Neural networks
Detectors
Object detection
train from scratch
deep neural networks
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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