Center of Microfluidic Optoelectronic Sensing, Dalian Maritime University, Dalian, 116026, China.
College of Information Science and Technology, Dalian Maritime University, Dalian, 116026 China.
Environ Sci Process Impacts. 2021 Oct 20;23(10):1516-1530. doi: 10.1039/d1em00159k.
Microalgae play a major role in the invasion of alien organisms with ballast water as a carrier, and traditional ballast water detection methods have many limitations in identifying microalgae species. Therefore, this paper proposes a method to identify microalgae in ballast water based on an Improved YOLOv3 model. The method first used a lightweight network MobileNet instead of the Darknet-53 network as the backbone network of feature extraction in the original YOLOv3 model. Secondly, improved spatial pyramid pooling (SPP) is introduced to pool and concatenate the multi-scale regional features so as to reduce the position error when detecting small objects. Then, by considering the overlap area of the bounding box, central point distance and aspect ratio, the Complete IoU (CIoU) algorithm is used to optimize the loss function of the YOLOv3 model. Finally, the proposed method is experimentally compared with other latest methods on the established dataset. The experimental results demonstrated that under the same conditions, this Improved YOLOv3 model achieves an average accuracy of 98.90%, and the detection efficiency is 8.59% higher than that of the original YOLOv3 model and is better than the existing methods. The average time of this method to identify a single image is 0.086 s, and it has a good detection effect on the identification of microalgae species.
微藻在以压载水为载体的外来生物入侵中扮演着重要的角色，而传统的压载水检测方法在识别微藻物种方面存在许多局限性。因此，本文提出了一种基于改进的 YOLOv3 模型的压载水中微藻识别方法。该方法首先使用轻量级网络 MobileNet 代替原始 YOLOv3 模型中的 Darknet-53 网络作为特征提取的骨干网络。其次，引入改进的空间金字塔池化（SPP）来池化和连接多尺度区域特征，以减少检测小物体时的位置误差。然后，通过考虑边界框的重叠面积、中心点距离和纵横比，使用 Complete IoU（CIoU）算法来优化 YOLOv3 模型的损失函数。最后，在建立的数据集上，将所提出的方法与其他最新方法进行了实验比较。实验结果表明，在相同条件下，改进后的 YOLOv3 模型的平均准确率达到 98.90%，检测效率比原始 YOLOv3 模型提高了 8.59%，优于现有的方法。该方法识别单张图像的平均时间为 0.086s，对微藻物种的识别具有良好的检测效果。
