Junior Franz Adeta
Computer Science Department, BINUS Graduate Program - Master of Computer Science, Bina Nusantara University, Jakarta, 10480, Indonesia.
Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta, 11480, Indonesia.
Heliyon. 2023 Jan 18;9(1):e13036. doi: 10.1016/j.heliyon.2023.e13036. eCollection 2023 Jan.
Research on oil palm detection has been carried out for years, but there are only a few research that have conducted research using video datasets and only focus on development using non-sequential image. The use of the video dataset aims to adjust to the detection conditions carried out in real time so that it can automatically harvest directly from oil palm trees to increase efficiency in harvesting. To solve this problem, in this research, we develop an object detection model using a video dataset in training and testing. We used the 3 series YOLOv4 architecture to develop the model using video. Model development is done by means of hyperparameter tuning and frozen layer with data augmentation consisting of photometric and geometric augmentation experiment. To validate the outcomes of the YOLOv4 model development, a comparison of SSD-MobileNetV2 FPN and EfficientDet-D0 was performed. The results obtained show that YOLOv4-Tiny 3L is the most suitable architecture for use in real time object detection conditions with an mAP of 90.56% for single class category detection and 70.21% for multi class category detection with a detection speed of almost 4× faster than YOLOv4-CSPDarknet53, 5× faster than SSD-MobileNetV2 FPN, and 9× faster than EfficientDet-D0.
对油棕检测的研究已经开展多年，但只有少数研究使用视频数据集进行研究，并且仅专注于使用非序列图像的开发。使用视频数据集的目的是适应实时进行的检测条件，以便能够直接从油棕树上自动收获，从而提高收获效率。为了解决这个问题，在本研究中，我们在训练和测试中使用视频数据集开发了一个目标检测模型。我们使用3系列YOLOv4架构通过视频来开发模型。模型开发通过超参数调整和带有由光度和几何增强实验组成的数据增强的冻结层来完成。为了验证YOLOv4模型开发的结果，对SSD-MobileNetV2 FPN和EfficientDet-D0进行了比较。获得的结果表明，YOLOv4-Tiny 3L是最适合用于实时目标检测条件的架构，对于单类类别检测，平均精度均值（mAP）为90.56%，对于多类类别检测为70.21%，检测速度比YOLOv4-CSPDarknet53快近4倍，比SSD-MobileNetV2 FPN快5倍，比EfficientDet-D0快9倍。
