The task of Transparent Tracking of Spermatozoa aims to detect and track sperm in a fluid environment. In addressing this challenge, we propose a framework that utilizes YOLOv8 and BoTSORT to address the issues related to the failure to detect small objects. Additionally, we suggest incorporating the equalization augmentation method to tackle problems related to imbalanced data. Our analysis results indicate that our methods can efectively resolve the imbalance issues in each data class and accurately detect small objects. This improvement significantly enhances the overall detection results.
Traditional manual sperm quality assessment through microscopy faces challenges like time
consumption, the need for expert skills, and variability in results.
Computer-Aided-SpermAnalysis (CASA) systems, introduced to automate sperm identification, tracking, and counting,
ofer an eficient alternative for male fertility evaluation. Despite their growing popularity,
CASA systems often struggle with inaccuracies. Previous deep learning approaches, including
those using YOLO-based models [
To address these shortcomings, we propose a novel approach in this challenge. Our work employs YOLOv8, a supervised model with the capability to efectively detect small objects, and apply equalization augmentation to solve the problem of an imbalanced dataset. Additionally, we assess the performance of this model using a simple tracking pipeline to underscore the crucial role of the detection model in this task.
In the 2023 MediaEval challenge [
YOLOv8 [
This comparative display in Figure 1 showcases the augmentation process designed to balance class representation in a sperm detection dataset. The first column presents the original microscopic images. The second column features these images annotated with blue bounding boxes identifying sperm, green for clusters, and red for small or pinhead spermatozoa. The third column demonstrates the images post-augmentation, indicating the enhancement of dataset diversity. The final column displays the augmented images with retained and updated annotations, ensuring accurate identification across the dataset’s newly diversified spectrum.
Table 1 shows that in our dataset, the "sperm" class predominates at 93.30%, while "cluster" and "small or pinhead" classes are underrepresented at 3.37% and 3.33%. This imbalance highlights the need for our Equalize Class Representation Augmentation method, aimed at balancing the dataset for better model training and enhancing detection accuracy for less frequent classes. Our "Equalize Class Representation Augmentation in Sperm Detection" method combats class imbalance by augmenting underrepresented classes to equal the dominant class’s frequency. It involves cropping regions from original images and randomly pasting them at non-overlapping locations, thereby increasing the presence of rarer classes. Updated annotations ensure dataset integrity, leading to a more balanced class representation and improved accuracy and generalization of the detection model.
It is worth noting that our Equalize Class Representation Augmentation method references
[
To track the detected sperm, we utilize BoT-SORT [
The tracking system is configured with specific parameters, including an initial association threshold of 0.5, a secondary association threshold of 0.1, an initialization threshold for new tracks set at 0.6, a track bufer duration of 30, and a track matching threshold of 0.8. For BoTSORT, the settings comprise a global motion compensation method named sparseOptFlow, a proximity threshold of 0.5, an appearance threshold of 0.25, and ReID model usage not enabled.
During both the training and inference stages, our model is resized to 640, following the
instructions from YOLO [
Results of our experiments on diferent sizes of pre-trained YOLOv8 model for the detection task with the validation set are presented in Table 2. The validation set has 5850 images, with the number of times class "sperm", "cluster", "small or pinhead" appear are 159305, 9606 and 5149, respectively.
Through experimenting with diferent sizes of pre-trained YOLOv8 models for detection, we found that larger models generalize better on the task, with a noticeable exception for the "cluster" class. YOLOv8x outperformed the "sperm" class, compared to smaller models - with a mAP50 of 0.719 and an mAP50-95 of 0.271 for "sperm" classes. YOLOv8x also performed best for detecting "small or pinhead" sperms with a mAP50 of 0.0919 and a mAP50-95 of 0.0361. However, the smaller the model, the better it can detect "cluster" sperms. Most notably, YOLOv8n had the highest precision and recall rate for cluster sperms - 0.253 and 0.112 respectively. By applying data augmentation, YOLOv8n detected cluster sperms best with a mAP50 of 0.14 and an mAP50-95 of 0.0384. Given the diference in model performance, ensembling is a possible choice.
In conclusion, in this challenge, we introduce a novel framework employing YOLOv8 and advancements in equalization augmentation to tackle issues related to sperm shape and class imbalance as observed in the aforementioned work. The experimental results highlight that our model efectively mitigates weaknesses in detecting small objects, ultimately yielding improved results in the tracking stage. Furthermore, incorporating our ofline augmentation methods into the dataset can assist the model in partially addressing issues related to class imbalance. The results from the experiments demonstrate the promise of our method to facilitate further research in sperm detection, contributing to enhanced performance of the tracking pipeline in general.
This research is funded by Viet Nam National University Ho Chi Minh City (VNU-HCM) under grant number DS2020-42-01.