Active matter consists of active agents which transform energy extracted from surroundings into momentum, producing a variety of collective phenomena. A model, synthetic active system composed of microtubule polymers driven by protein motors spontaneously forms a liquid-crystalline nematic phase. Extensile stress created by the protein motors precipitates continuous buckling and folding of the microtubules creating motile topological defects and turbulent fluid flows. Defect motion is determined by the rheological properties of the material; however, these remain largely unquantified. Measuring defects dynamics can yield fundamental insights into active nematics, a class of materials that include bacterial films and animal cells. Current methods for defect detection lack robustness and precision, and require fine-tuning for datasets with different visual quality.   In this study, we applied Deep Learning to train a defect detector to automatically analyze microscopy videos of the microtubule active nematic.   Experimental results indicate that our method is robust and accurate. It is expected to significantly increase the amount of video data that can be processed. While YOLO is the state-of-the-art object detection algorithms in images, it is not designed specifically for video. In other words, YOLO is not able to make detection decisions based on information from previous and next frames. One set of data in the active nematics experiments is usually a video containing up to twenty thousand frames. Typically, defects positions do not drastically change among consecutive frames, and the active system as a whole move relatively smoothly. Therefore, improving the design of YOLO to utilize the connection between frames will be expected to improve the detecting results. This intuition is supported by research carried out by BID9 , who developed "tubelets" with R-CNN to incorporate temporal and contextual information into the decision making process. The method, named T-CNN is a successful design which has boosted the detecting results generated under R-CNN framework. Therefore, as our next step, we will incorporate the temporal and contextual information into YOLO's pipeline in a similar way in order to improve the detecting results in our dataset. Based on experimental results, our method has been shown to be the state-of-the-art in defects detection algorithms. Our method's advantages include significantly faster processing rate, higher F1-score on detecting results, and straightforward execution that could be operated by any researchers with simple python skills.We have shown the viability of deep learning's application in soft matter. Most of experiments in the field of soft matter involve experimental data in the form of images or videos and object detection is one of the most common tasks physicists face. We hope this paper could provide an effective alternative for physicists when they try to tackle similar tasks.
An interesting application of CNN in soft condensed matter physics experiments. The authors demonstrate that a deep learning approach offers improvement to both the identification accuracy and rate at which defects can be identified of nematic liquid crystals. Apply a well known neural model (YOLO) to detect bounding boxes of objects in images.