Gu Hao, Gan Dongmei, Chen Ming, Feng Guofu
Key Laboratory of Fisheries Information, Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Hucheng Ring Road 999, Shanghai 201306, China.
Animals (Basel). 2024 Jul 21;14(14):2128. doi: 10.3390/ani14142128.
The cultivation of the Chinese mitten crab () is an important component of China's aquaculture industry and also a field of concern worldwide. It focuses on the selection of high-quality, disease-free juvenile crabs. However, the early maturity rate of more than 18.2% and the mortality rate of more than 60% make it difficult to select suitable juveniles for adult culture. The juveniles exhibit subtle distinguishing features, and the methods for differentiating between sexes vary significantly; without training from professional breeders, it is challenging for laypersons to identify and select the appropriate juveniles. Therefore, we propose a task-aligned detection algorithm for identifying one-year-old precocious Chinese mitten crabs, named R-TNET. Initially, the required images were obtained by capturing key frames, and then they were annotated and preprocessed by professionals to build a training dataset. Subsequently, the ResNeXt network was selected as the backbone feature extraction network, with Convolutional Block Attention Modules (CBAMs) and a Deformable Convolution Network (DCN) embedded in its residual blocks to enhance its capability to extract complex features. Adaptive spatial feature fusion (ASFF) was then integrated into the feature fusion network to preserve the detailed features of small targets such as one-year-old precocious Chinese mitten crab juveniles. Finally, based on the detection head proposed by task-aligned one-stage object detection, the parameters of its anchor alignment metric were adjusted to detect, locate, and classify the crab juveniles. The experimental results showed that this method achieves a mean average precision (mAP) of 88.78% and an F1-score of 97.89%. This exceeded the best-performing mainstream object detection algorithm, YOLOv7, by 4.17% in mAP and 1.77% in the F1-score. Ultimately, in practical application scenarios, the algorithm effectively identified one-year-old precocious Chinese mitten crabs, providing technical support for the automated selection of high-quality crab juveniles in the cultivation process, thereby promoting the rapid development of aquaculture and agricultural intelligence in China.
中华绒螯蟹的养殖是中国水产养殖业的重要组成部分,也是全球关注的领域。它侧重于选择优质、无病的幼蟹。然而,超过18.2%的早熟率和超过60%的死亡率使得难以选择适合成年养殖的幼蟹。幼蟹具有细微的区别特征,区分性别的方法差异很大;未经专业养殖人员培训,外行人很难识别和选择合适的幼蟹。因此,我们提出了一种用于识别一岁早熟中华绒螯蟹的任务对齐检测算法,名为R-TNET。首先,通过捕获关键帧获得所需图像,然后由专业人员进行标注和预处理以构建训练数据集。随后,选择ResNeXt网络作为骨干特征提取网络,在其残差块中嵌入卷积块注意力模块(CBAM)和可变形卷积网络(DCN)以增强其提取复杂特征的能力。然后将自适应空间特征融合(ASFF)集成到特征融合网络中,以保留一岁早熟中华绒螯蟹幼蟹等小目标的详细特征。最后,基于任务对齐的单阶段目标检测提出的检测头,调整其锚点对齐度量的参数,以检测、定位和分类蟹幼体。实验结果表明,该方法的平均精度均值(mAP)为88.78%,F1分数为97.89%。这在mAP上比性能最佳的主流目标检测算法YOLOv7高出4.17%,在F1分数上高出1.77%。最终,在实际应用场景中,该算法有效地识别了一岁早熟中华绒螯蟹,为养殖过程中优质蟹幼体的自动化选择提供了技术支持,从而推动了中国水产养殖和农业智能化的快速发展。