卷积神经网络在图像目标检测中的应用

The Application of Convolutional Neural Networks in Image Object Detection

分析卷积神经网络(Convolutional Neural Networks,CNN)在图像目标检测中的性能,重点比较AlexNet、GoogleNet和ResNet50这3个流行模型在不同数据集上的表现。在CIFAR-100和CIFAR-10数据集上,GoogleNet和ResNet50表现出更精确的物体识别能力,而AlexNet相对稍弱。这些结果有助于深入了解CNN在图像识别任务中的性能和适用性。

This article analyzes the performance of Convolutional Neural Networks(CNN)in image object detection,with a focus on comparing the performance of three popular models,AlexNet,GoogleNet,and ResNet50,on different datasets.On the CIFAR-100 and CIFAR-10 datasets,GoogleNet and ResNet50 exhibit more accurate object recognition capabilities,while AlexNet is relatively weaker.These results help to provide insight into the performance and applicability of CNN in image recognition tasks.