Dual‐attention global domain adaptation for mariculture image enhancement

DRCNet: Dynamic Image Restoration Contrastive Network

Fuzzy Discriminative Block Representation Learning for Image Feature Extraction

Towards fusing fuzzy discriminative projection and representation learning for image classification

CMFTNet: Multiple fish tracking based on counterpoised JointNet

Tied Bilateral learning for Aquaculture Image Enhancement

基于多源信息融合的农业空地一体化研究综述

无人机（UAV, Unmanned aerial vehicle）作为一种灵活、高效的农业环境信息和作物生长信息获取技术的载体，近年来在农业生产和科研领域得到了广泛的应用。随着农业4.0的来临，无人机搭载感知成像设备已经成为智慧农业中信息获取的重要技术手段，与地上或地下传感器等共同构成空地一体化系统，为智能化农业管理提供数据支持和决策依据。多源信息融合是提高无人机感知能力的关键技术之一，其研究对于无人机的应用有着重要意义。与单一信息获取相比，基于多源数据融合的方法，将多源性的各类信息进行各种运算与处理，来提取目标的特征信息，以便进行分析与理解，最终实现对目标的识别、检测和控制等。论文总结了国内外 20 多年来有代表性的相关研究和解决方案，从无人机影像背景复杂、目标较小、视场大、目标具有旋转性的特点出发，对无人机目标检测近期的研究进行了归纳和分析。最后讨论了存在的问题，给出了今后的研究趋势与发展方向判断。

S-FPN: A shortcut feature pyramid network for sea cucumber detection in underwater images

Vegetable Recognition and Classification Based on Improved VGG Deep Learning Network Model

To improve the accuracy of automatic recognition and classification of vegetables, this paper presents a method of recognition and classification of vegetable image based on deep learning, using the open source deep learning framework of Caffe, the improved VGG network model was used to train the vegetable image data set. We propose to combine the output feature of the first two fully connected layers (VGG-M). The Batch Normalization layers are added to the VGG-M network to improve the convergence speed and accuracy of the network (VGG-M-BN). The experimental verification, this paper method in the test data set on the classification of recognition accuracy rate as high as 96.5%, compared with VGG network (92.1%) and AlexNet network (86.3%), the accuracy rate has been greatly improved. At the same time, increasing the Batch Normalization layers make the network convergence speed nearly tripled. Improve the generalization ability of the model by expanding the scale of the training data set. Using VGG-M-BN network to train different number of vegetable image data sets, the experimental results show that the recognition accuracy decreases as the number of data sets decreases. By contrasting the activation functions, it is verified that the Rectified Linear Unit (ReLU) activation function is better than the traditional Sigmoid and Tanh functions in VGG-M-BN networks. The paper also verifies that the classification accuracy of VGG-M-BN network is improved due to the increase of batch_size.