YOLOv7 Tiny improvement for bull sperm detection - Nashirin, Wafi Khoerun; Zaman, Azzam Badruz; Hidayatullah, Priyanto; Ekawijana, Ardhian (Akademisi dan Saintis Indonesia (ASASI), 2024)
YOLO (You Only Look Once) is a prominent deep learning model used in object detection due to its high detection accuracy and speed. Nonetheless, in detecting bull sperm, YOLOv7 Tiny performance suffers because of the unique characteristics of bull sperm: its tiny size and the large quantity of sperm. YOLOv7 Tiny's performance can be improved by adjusting based on its unique characteristics. This study proposes a modified YOLOv7 Tiny model to detect bull sperm with higher accuracy. The main objective of this research is to increase the accuracy of YOLOv7 Tiny in detecting and counting bull sperm. The YOLOv7 Tiny architecture will be modified based on the characteristics of the object to be detected, specifically bull sperm. Several architectural parts were deleted, the anchor box's size was changed, and the grid cell's size was changed. The omitted architecture parts are the ones used for detecting large and medium-sized objects. The anchor box and grid cell sizes will be altered to fit the size of the object. Accuracy is measured using mean average precision (mAP). The modified YOLOv7 Tiny will be evaluated in comparison to the original YOLOv7 Tiny. In our experiment, we produced 65.8 mAP with an inference time of 14.4 ms on the test dataset. When detecting bull sperm, the modified model is 1.3 points more accurate and 1.23x faster than YOLOv7 Tiny. The size of the modified model file is likewise decreased by 84.2%.