Refined feature fusion for in-field high-density and multi-scale rice panicle counting in UAV images

The yield of rice crops is strongly correlated to the number of panicles per unit area. Computer vision techniques have been utilized in previous studies to count the number of panicles automatically. However, when using images captured by Unmanned Aerial Vehicles (UAVs), the height and coverage area of the UAVs can cause shrinkage of the rice panicle objects, leading to errors in feature learning and reduced counting efficacy. In addition, there is a considerable relative size difference in rice ears, which can further affect counting accuracy. To address these issues, this paper proposes an algorithm named Refined Feature Fusion for Panicle Counting (RFF-PC). This algorithm utilizes a more refined scale division by extracting and fusing optimal features according to the object size distribution. Firstly, the number of rice ears with different sizes is quantified, and a fine division of the scale is made to calculate the receptive field size of different features that need to be fused. Secondly, multi-scale convolution generates features at each layer and feature pyramid fusion fuses more appropriate features at different layers to improve the ability to represent objects in complex multi-scale. Redundant information is removed through channel attention. Additionally, a refined Gaussian is used to generate ground truth close to the shape of real rice panicles. Over the UFPC2019 dataset captured at the height of 5 meters, the average counting accuracy of RFF-PC is 89.80%, and the counting accuracy only
decreases from 94.33% to 90.58% over the images with an increased number of rice ears from 180 to 260, which outperforms several state-of-the-art algorithms, including MCNN, CSRNet, TasselNetV2+, DSNet, and DMCount.