Liaoning Key Laboratory of Marine Sensing and Intelligent Detection, Dalian Maritime University, 116026, Dalian, China; Department of Information Science and Technology, Dalian Maritime University, 116026, Dalian, China.
Liaoning Key Laboratory of Marine Sensing and Intelligent Detection, Dalian Maritime University, 116026, Dalian, China; Department of Information Science and Technology, Dalian Maritime University, 116026, Dalian, China.
J Environ Manage. 2023 Nov 1;345:118802. doi: 10.1016/j.jenvman.2023.118802. Epub 2023 Aug 15.
Microplastics refer to plastic particles measuring less than 5 mm, which has led to serious environmental problem and the detection of these tiny particles is crucial for understanding the corresponding distribution and impact on the marine environment. In this paper, an improved faster region-based convolutional neural network (R-CNN) model was developed for the identification and detection of microplastic particles. In the proposed model, the residual network-50 (ResNet-50) is employed as the backbone with the replacement of the traditional one to enhance the feature extraction capability and the feature pyramid networks (FPN) module is introduced together for solving the multi-scale target detection. By using the improved Faster R-CNN model, the network model performance is enhanced where the average confidence of detecting unique microplastic particles in the marine environment reaches as high as 99%. Moreover, the microparticles mixture was bounded precisely via the predicted bounding boxes without missing detection and wrong detection. In this way, the successful identification of polystyrene microplastic particles from the particles suspension with similar shapes but various conditions of backgrounds, brightness, distributions and object sizes, was achieved by employing the proposed improved Faster R-CNN model, enabling the accurate detection of microplastic particles in marine environment.
微塑料是指粒径小于 5 毫米的塑料颗粒，这导致了严重的环境问题，因此检测这些微小颗粒对于了解其相应的分布和对海洋环境的影响至关重要。本文提出了一种改进的快速区域卷积神经网络（R-CNN）模型，用于识别和检测微塑料颗粒。在提出的模型中，使用残差网络-50（ResNet-50）作为骨干网络，取代了传统的骨干网络，以增强特征提取能力，并引入特征金字塔网络（FPN）模块，用于解决多尺度目标检测问题。通过使用改进的 Faster R-CNN 模型，提高了网络模型的性能，在海洋环境中检测独特微塑料颗粒的平均置信度高达 99%。此外，通过预测的边界框可以精确地确定微粒子混合物，没有漏检和误检。通过采用所提出的改进的 Faster R-CNN 模型，可以成功识别出具有相似形状但背景、亮度、分布和物体大小等各种条件的粒子悬浮液中的聚苯乙烯微塑料颗粒，从而实现了对海洋环境中微塑料颗粒的准确检测。
