Yu Junwei, Zhai Fupin, Liu Nan, Shen Yi, Pan Quan
School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China.
College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
Insects. 2023 Jan 17;14(2):99. doi: 10.3390/insects14020099.
As insect infestation is the leading factor accounting for nutritive and economic losses in stored grains, it is important to detect the presence and number of insects for the sake of taking proper control measures. Inspired by the human visual attention mechanism, we propose a U-net-like frequency-enhanced saliency (FESNet) detection model, resulting in the pixelwise segmentation of grain pests. The frequency clues, as well as the spatial information, are leveraged to enhance the detection performance of small insects from the cluttered grain background. Firstly, we collect a dedicated dataset, GrainPest, with pixel-level annotation after analyzing the image attributes of the existing salient object detection datasets. Secondly, we design a FESNet with the discrete wavelet transformation (DWT) and the discrete cosine transformation (DCT), both involved in the traditional convolution layers. As current salient object detection models will reduce the spatial information with pooling operations in the sequence of encoding stages, a special branch of the discrete wavelet transformation (DWT) is connected to the higher stages to capture accurate spatial information for saliency detection. Then, we introduce the discrete cosine transform (DCT) into the backbone bottlenecks to enhance the channel attention with low-frequency information. Moreover, we also propose a new receptive field block (NRFB) to enlarge the receptive fields by aggregating three atrous convolution features. Finally, in the phase of decoding, we use the high-frequency information and aggregated features together to restore the saliency map. Extensive experiments and ablation studies on our dataset, GrainPest, and open dataset, Salient Objects in Clutter (SOC), demonstrate that the proposed model performs favorably against the state-of-the-art model.
由于虫害是导致储存谷物营养和经济损失的主要因素，为采取适当的控制措施，检测昆虫的存在和数量非常重要。受人类视觉注意力机制的启发，我们提出了一种类似U-net的频率增强显著性（FESNet）检测模型，用于对谷物害虫进行逐像素分割。利用频率线索以及空间信息，以提高在杂乱谷物背景下对小昆虫的检测性能。首先，在分析现有显著目标检测数据集的图像属性后，我们收集了一个专门的数据集GrainPest，并进行了像素级标注。其次，我们设计了一种FESNet，它在传统卷积层中同时使用离散小波变换（DWT）和离散余弦变换（DCT）。由于当前的显著目标检测模型在编码阶段会通过池化操作减少空间信息, 因此将离散小波变换（DWT）的一个特殊分支连接到更高阶段，以捕获用于显著性检测的准确空间信息。然后，我们将离散余弦变换（DCT）引入主干瓶颈，以利用低频信息增强通道注意力。此外，我们还提出了一种新的感受野块（NRFB），通过聚合三个空洞卷积特征来扩大感受野。最后，在解码阶段，我们将高频信息和聚合特征一起用于恢复显著性图。在我们的数据集GrainPest和开放数据集杂乱场景中的显著目标（SOC）上进行的大量实验和消融研究表明，所提出的模型优于当前的先进模型。
