The Application of the Dilated Convolution based on Small Object Detection

Object detection is an important part of computer vision applications. It is widely used in robot navigation, intelligent video surveillance, industrial testing, aerospace and other fields. Full application of computer vision can reduce the consumption of human capital. At present, most deep learning-based methods focus on large objects, and we know that large objects always account a large part of an image. Therefore, these methods are not ideal for detecting small objects. The reason is that small objects carry very little information and their ability to express features are also weak. To solve this problem, we use our model and try to enlarge the resolution of feature maps by applying the dilated convolution. The results show that it improves the accuracy of detecting small objects.