Phenotyping is the process of measuring an organism's observable traits. Manual phenotyping of crops is a labor-intensive, time-consuming, costly, and error prone process. Accurate, automated, high-throughput phenotyping can relieve a huge burden in the crop breeding pipeline. In this paper, we propose a scalable, high-throughput approach to automatically count and segment panicles (heads), a key phenotype, from aerial sorghum crop imagery. Our counting approach uses the image density map obtained from dot or region annotation as the target with a novel deep convolutional neural network architecture. We also propose a novel instance segmentation algorithm using the estimated density map, to identify the individual panicles in the presence of occlusion. With real Sorghum aerial images, we obtain a mean absolute error (MAE) of 1.06 for counting which is better than using well-known crowd counting approaches such as CCNN, MCNN and CSRNet models. The instance segmentation model also produces respectable results which will be ultimately useful in reducing the manual annotation workload for future data.
We have used the CCNN, MCNN and CSRNet models in our experiments, but it should be noted that none of these models achieve the best performance on the common crowd counting benchmarks. The CSRNet was the best performing model when published in CVPR 2018, but has since been surpassed on several benchmarks by SANet @cite_37 , ic-CNN @cite_58 and by ASD @cite_8 . Table shows the performance of these 6 systems on the large and popular UCF CC 50 benchmark. The code for CCNN, MCNN and CSRNet have been released, but we are unaware of codes for SANet, ic-CNN, and ASD, which makes it almost impossible for us to make comparisons. We believe CSRNet is the best system for crowd counting that have been independently verified.
@cite_37 @cite_58 @cite_8
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This paper aims to develop a method than can accurately estimate the crowd count from an individual image with arbitrary crowd density and arbitrary perspective. To this end, we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map. The proposed MCNN allows the input image to be of arbitrary size or resolution. By utilizing filters with receptive fields of different sizes, the features learned by each column CNN are adaptive to variations in people head size due to perspective effect or image resolution. Furthermore, the true density map is computed accurately based on geometry-adaptive kernels which do not need knowing the perspective map of the input image. Since exiting crowd counting datasets do not adequately cover all the challenging situations considered in our work, we have collected and labelled a large new dataset that includes 1198 images with about 330,000 heads annotated. On this challenging new dataset, as well as all existing datasets, we conduct extensive experiments to verify the effectiveness of the proposed model and method. In particular, with the proposed simple MCNN model, our method outperforms all existing methods. In addition, experiments show that our model, once trained on one dataset, can be readily transferred to a new dataset. We propose a network for Congested Scene Recognition called CSRNet to provide a data-driven and deep learning method that can understand highly congested scenes and perform accurate count estimation as well as present high-quality density maps. The proposed CSRNet is composed of two major components: a convolutional neural network (CNN) as the front-end for 2D feature extraction and a dilated CNN for the back-end, which uses dilated kernels to deliver larger reception fields and to replace pooling operations. CSRNet is an easy-trained model because of its pure convolutional structure. We demonstrate CSRNet on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the WorldEXPO'10 dataset, and the UCSD dataset) and we deliver the state-of-the-art performance. In the ShanghaiTech Part_B dataset, CSRNet achieves 47.3 lower Mean Absolute Error (MAE) than the previous state-of-the-art method. We extend the targeted applications for counting other objects, such as the vehicle in TRANCOS dataset. Results show that CSRNet significantly improves the output quality with 15.4 lower MAE than the previous state-of-the-art approach. The task of crowd counting is to automatically estimate the pedestrian number in crowd images. To cope with the scale and perspective changes that commonly exist in crowd images, state-of-the-art approaches employ multi-column CNN architectures to regress density maps of crowd images. Multiple columns have different receptive fields corresponding to pedestrians (heads) of different scales. We instead propose a scale-adaptive CNN (SaCNN) architecture with a backbone of fixed small receptive fields. We extract feature maps from multiple layers and adapt them to have the same output size; we combine them to produce the final density map. The number of people is computed by integrating the density map. We also introduce a relative count loss along with the density map loss to improve the network generalization on crowd scenes with few pedestrians, where most representative approaches perform poorly on. We conduct extensive experiments on the ShanghaiTech, UCF CC 50 and WorldExpo'10 datasets as well as a new dataset SmartCity that we collect for crowd scenes with few people. The results demonstrate significant improvements of SaCNN over the state-of-the-art. In this paper we address the problem of counting objects instances in images. Our models are able to precisely estimate the number of vehicles in a traffic congestion, or to count the humans in a very crowded scene. Our first contribution is the proposal of a novel convolutional neural network solution, named Counting CNN (CCNN). Essentially, the CCNN is formulated as a regression model where the network learns how to map the appearance of the image patches to their corresponding object density maps. Our second contribution consists in a scale-aware counting model, the Hydra CNN, able to estimate object densities in different very crowded scenarios where no geometric information of the scene can be provided. Hydra CNN learns a multiscale non-linear regression model which uses a pyramid of image patches extracted at multiple scales to perform the final density prediction. We report an extensive experimental evaluation, using up to three different object counting benchmarks, where we show how our solutions achieve a state-of-the-art performance.