DeepSperm: A robust and real-time bull sperm-cell detection in densely populated semen videos

• URL: http://arxiv.org/abs/2003.01395v1
• Date: Tue, 3 Mar 2020 09:05:05 GMT
• Title: DeepSperm: A robust and real-time bull sperm-cell detection in densely populated semen videos
• Authors: Priyanto Hidayatullah, Xueting Wang, Toshihiko Yamasaki, Tati L.E.R. Mengko, Rinaldi Munir, Anggraini Barlian, Eros Sukmawati, Supraptono Supraptono
• Abstract summary: This study proposes an architecture, called DeepSperm, that solves the challenges and is more accurate and faster than state-of-the-art architectures. In our experiment, we achieve 86.91 mAP on the test dataset and a processing speed of 50.3 fps.
• Score: 26.494850349599528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background and Objective: Object detection is a primary research interest in computer vision. Sperm-cell detection in a densely populated bull semen microscopic observation video presents challenges such as partial occlusion, vast number of objects in a single video frame, tiny size of the object, artifacts, low contrast, and blurry objects because of the rapid movement of the sperm cells. This study proposes an architecture, called DeepSperm, that solves the aforementioned challenges and is more accurate and faster than state-of-the-art architectures. Methods: In the proposed architecture, we use only one detection layer, which is specific for small object detection. For handling overfitting and increasing accuracy, we set a higher network resolution, use a dropout layer, and perform data augmentation on hue, saturation, and exposure. Several hyper-parameters are tuned to achieve better performance. We compare our proposed method with those of a conventional image processing-based object-detection method, you only look once (YOLOv3), and mask region-based convolutional neural network (Mask R-CNN). Results: In our experiment, we achieve 86.91 mAP on the test dataset and a processing speed of 50.3 fps. In comparison with YOLOv3, we achieve an increase of 16.66 mAP point, 3.26 x faster on testing, and 1.4 x faster on training with a small training dataset, which contains 40 video frames. The weights file size was also reduced significantly, with 16.94 x smaller than that of YOLOv3. Moreover, it requires 1.3 x less graphical processing unit (GPU) memory than YOLOv3. Conclusions: This study proposes DeepSperm, which is a simple, effective, and efficient architecture with its hyper-parameters and configuration to detect bull sperm cells robustly in real time. In our experiment, we surpass the state of the art in terms of accuracy, speed, and resource needs.

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