Research on the Method of Rice Seed Density Detection in Rice Seedling Tray Based on MCNN

Abstract

Due to the small size of the rice seed images in the rice seedling tray and hole tray, perspective distortion occurs, and the size of each target in the image varies. Traditional convolutional neural networks using receptive field convolution kernels of the same size cannot accurately capture target features. Although some scholars have addressed this issue by combining density maps extracted from image blocks of different resolutions or feature maps obtained through convolutional filters of different sizes. By indiscriminately fusing information from all scales, these methods ignore the fact that the scales in the image are continuously changing. Training a classifier to predict the receptive field size used locally can solve this problem, but it is not an end-to-end training approach and cannot effectively address rapid scale changes. This article introduces multi-scale convolutional neural networks (MCNN), which explicitly extract features on multiple receptive field sizes and learn the importance of each such feature at each image position. This method adaptively encodes the scale of contextual information required to predict target density, thereby explaining potential rapid scale changes.