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

Abstract

Background and ObjectiveObject detection is a primary research interest in computer vision. Sperm-cell detection in a densely populated bull semen microscopic observation video presents challenges that are more difficult than those presented by other general object-detection cases. These challenges include partial occlusion, vast number of objects in a single video frame, tiny size of the object, artifacts, low contrast, low video resolution, and blurry objects because of the rapid movement of the sperm cells. This study proposes a deep neural network architecture, called DeepSperm, that solves the aforementioned problems and is more accurate and faster than state-of-the-art architectures. MethodsIn 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 input network resolution, use a dropout layer, and perform data augmentation on saturation and exposure. Several hyper-parameters are tuned to achieve better performance. Mean average precision (mAP), confusion matrix, precision, recall, and F1-score are used to measure accuracy. Frame per second (fps) is used to measure speed. We compare our proposed method with you only look once (YOLO) v3 and YOLOv4. ResultsIn our experiment, we achieve 94.11 mAP on the test dataset, F1-score of 0.93, and a processing speed of 51.9 fps. In comparison with YOLOv4, our proposed method is 2.18 x faster on testing, and 2.9 x faster on training with a small dataset, while achieving comparative detection accuracy. The weights file size was also reduced significantly, with one-twentieth that of YOLOv4. Moreover, it requires a 1.07 x less graphical processing unit (GPU) memory than YOLOv4. ConclusionsThis study proposes DeepSperm, which is a simple, effective, and efficient deep neural network architecture with its hyper-parameters and configuration to detect bull sperm cells robustly in real time. In our experiments, we surpass the state-of-the-art in terms of accuracy, speed, and resource needs.