Dong Yan, Liu Yundong, Kang Haonan, Li Chunlei, Liu Pengcheng, Liu Zhoufeng
School of Electronic and Information Engineering, Zhongyuan University of Technology, ZhengZhou, China.
Department of Statistics and Data Science, National University of Singapore, Singapore.
PeerJ Comput Sci. 2022 Apr 5;8:e931. doi: 10.7717/peerj-cs.931. eCollection 2022.
Advancements in deep neural networks have made remarkable leap-forwards in crop detection. However, the detection of wheat ears is an important yet challenging task due to the complex background, dense targets, and overlaps between wheat ears. Currently, many detectors have made significant progress in improving detection accuracy. However, some of them are not able to make a good balance between computational cost and precision to meet the needs of deployment in real world. To address these issues, a lightweight and efficient wheat ear detector with Shuffle Polarized Self-Attention (SPSA) is proposed in this paper. Specifically, we first utilize a lightweight backbone network with asymmetric convolution for effective feature extraction. Next, SPSA attention is given to adaptively select focused positions and produce a more discriminative representation of the features. This strategy introduces polarized self-attention to spatial dimension and channel dimension and adopts Shuffle Units to combine those two types of attention mechanisms effectively. Finally, the TanhExp activation function is adopted to accelerate the inference speed and reduce the training time, and CIOU loss is used as the border regression loss function to enhance the detection ability of occlusion and overlaps between targets. Experimental results on the Global Wheat Head Detection dataset show that our method achieves superior detection performance compared with other state-of-the-art approaches.
深度神经网络的进步在作物检测方面取得了显著进展。然而，由于背景复杂、目标密集以及麦穗之间的重叠，麦穗检测是一项重要但具有挑战性的任务。目前，许多检测器在提高检测精度方面取得了重大进展。然而，其中一些检测器无法在计算成本和精度之间取得良好平衡，以满足实际应用中的部署需求。为了解决这些问题，本文提出了一种具有洗牌极化自注意力（SPSA）的轻量级高效麦穗检测器。具体来说，我们首先使用具有非对称卷积的轻量级主干网络进行有效的特征提取。接下来，给予SPSA注意力以自适应选择聚焦位置并产生更具判别力的特征表示。该策略将极化自注意力引入空间维度和通道维度，并采用洗牌单元有效地结合这两种注意力机制。最后，采用TanhExp激活函数来加速推理速度并减少训练时间，使用CIOU损失作为边界回归损失函数来增强对目标之间遮挡和重叠的检测能力。在全球麦穗检测数据集上的实验结果表明，与其他现有先进方法相比，我们的方法具有卓越的检测性能。
