目标检测
计算机视觉
随机梯度下降算法
计算机科学
人工智能
梯度下降
领域(数学)
集合(抽象数据类型)
对象(语法)
功能(生物学)
数据挖掘
机器学习
模式识别(心理学)
数学
人工神经网络
进化生物学
纯数学
生物
程序设计语言
作者
Yu-Fang Huang,Tsung-Jung Liu,Kuan-Hsien Liu
标识
DOI:10.1109/icce-taiwan55306.2022.9869118
摘要
With the popularization of self-driving cars, more and more researches have been done on road object detection. However, many challenges remain to be resolved, such as the detection accuracy of small objects in the long distance. Therefore, we propose an algorithm based on YOLO-R to improve the detection accuracy to deal with the actual situation in this field. First, we set some conditions and propose some methods to balance the problem of extremely unbalanced size among each target label. Secondly, the Mish activation function is selected for training. Finally, we use the stochastic gradient descent (SGD) method to ensure that the best global solution can be obtained, and experiments on the BDD100k dataset show that our method has better results than other models in this dataset.
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