Background Grain yield of wheat is greatly associated with the population of wheat spikes, i.e., s p i k e n u m b e r m - 2 . To obtain this index in a reliable and efficient way, it is necessary to count wheat spikes accurately and automatically. Currently computer vision technologies have shown great potential to automate this task effectively in a low-end manner. In particular, counting wheat spikes is a typical visual counting problem, which is substantially studied under the name of object counting in Computer Vision. TasselNet, which represents one of the state-of-the-art counting approaches, is a convolutional neural network-based local regression model, and currently benchmarks the best record on counting maize tassels. However, when applying TasselNet to wheat spikes, it cannot predict accurate counts when spikes partially present. Results In this paper, we make an important observation that the counting performance of local regression networks can be significantly improved via adding visual context to the local patches. Meanwhile, such context can be treated as part of the receptive field without increasing the model capacity. We thus propose a simple yet effective contextual extension of TasselNet-TasselNetv2. If implementing TasselNetv2 in a fully convolutional form, both training and inference can be greatly sped up by reducing redundant computations. In particular, we collected and labeled a large-scale wheat spikes counting (WSC) dataset, with 1764 high-resolution images and 675,322 manually-annotated instances. Extensive experiments show that, TasselNetv2 not only achieves state-of-the-art performance on the WSC dataset ( 91.01 % counting accuracy) but also is more than an order of magnitude faster than TasselNet (13.82 fps on 912 × 1216 images). The generality of TasselNetv2 is further demonstrated by advancing the state of the art on both the Maize Tassels Counting and ShanghaiTech Crowd Counting datasets. Conclusions This paper describes TasselNetv2 for counting wheat spikes, which simultaneously addresses two important use cases in plant counting: improving the counting accuracy without increasing model capacity, and improving efficiency without sacrificing accuracy. It is promising to be deployed in a real-time system with high-throughput demand. In particular, TasselNetv2 can achieve sufficiently accurate results when training from scratch with small networks, and adopting larger pre-trained networks can further boost accuracy. In practice, one can trade off the performance and efficiency according to certain application scenarios. Code and models are made available at: https://tinyurl.com/TasselNetv2.

中文翻译：

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TasselNetv2：使用上下文增强局部回归网络对小麦穗进行现场计数。

背景 小麦的产量与小麦穗的数量密切相关，即穗数m - 2 。为了以可靠和有效的方式获得该指标，需要准确、自动地计算小麦穗数。目前，计算机视觉技术已经显示出以低端方式有效地自动化这项任务的巨大潜力。特别是，计算小麦穗数是一个典型的视觉计数问题，在计算机视觉中以物体计数的名义进行了大量研究。TasselNet 代表了最先进的计数方法之一，是一种基于卷积神经网络的局部回归模型，目前对玉米流苏计数的最佳记录进行了基准测试。然而，当将 TasselNet 应用于小麦穗时，当穗部分存在时，它无法预测准确的计数。结果在本文中，我们做了一个重要的观察，即通过向局部块添加视觉上下文可以显着提高局部回归网络的计数性能。同时，这种上下文可以被视为感受野的一部分，而无需增加模型容量。因此，我们提出了一个简单而有效的 TasselNet-TasselNetv2 上下文扩展。如果以完全卷积的形式实现 TasselNetv2，则可以通过减少冗余计算来大大加快训练和推理的速度。特别是，我们收集并标记了一个大规模小麦穗计数 (WSC) 数据集，其中包含 1764 个高分辨率图像和 675,322 个手动注释实例。大量实验表明，TasselNetv2 不仅在 WSC 数据集（91. 01 % 的计数准确率），但也比 TasselNet 快一个数量级（在 912 × 1216 图像上为 13.82 fps）。TasselNetv2 的通用性通过推进玉米流苏计数和上海科技人群计数数据集的最新技术得到进一步证明。结论 本文描述了用于计算小麦穗数的 TasselNetv2，它同时解决了植物计数中的两个重要用例：在不增加模型容量的情况下提高计数精度，以及在不牺牲精度的情况下提高效率。它有望部署在具有高吞吐量需求的实时系统中。特别是 TasselNetv2 在使用小型网络从头开始训练时可以达到足够准确的结果，而采用更大的预训练网络可以进一步提高准确度。在实践中，可以根据特定的应用场景来权衡性能和效率。代码和模型可在以下网址获得：https://tinyurl.com/TasselNetv2。