Details

Autor(en) / Beteiligte

• Wang, Junsheng
• Dong, Jianhong
• Tang, Mengrao
• Yao, Junzhu
• Li, Xuan
• Kong, Dejian
• Zhao, Kai

Titel

Identification and detection of microplastic particles in marine environment by using improved faster R–CNN model

Ist Teil von

• Journal of environmental management, 2023-11, Vol.345, p.118802-118802, Article 118802

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• 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. [Display omitted] •The deep learning method is employed for microparticle particles identification.•Microparticles micrographs can be distinguished with improved Faster R–CNN model.•ResNet-50 and FPN are integrated to enhance feature extraction and detection.•The average confidence of detecting microplastic particles reaches as high as 99%.