Abstract
Agriculture presents challenges in automation, especially so in vision systems. Varying lighting conditions, sporadic diversity, and large amounts of noise create difficulty in detecting target objects. Our Mummy Nuts datasets present these challenges in tiny scale, camouflaged, dark, or even hidden target objects. However, the most recent advancements in Convolutional Neural Networks (CNN) in the object detection task have become increasingly accurate and robust. As there are many different CNNs, selecting which CNN will perform the best may become challenging. This paper proposes a two-dimensional benchmarking methodology to evaluate five popular CNN models (YOLOv3, YOLOv5, CenterNet, Faster R-CNN, and MobileNet SSD) on two NVIDIA GPUs (Tesla T4 and A100). Our benchmarking methodology evaluates accuracy across all models and performance among models on each GPU. Our results show the benefits of selecting models using our Augmented dataset over the Original dataset. CNN Models overall see an increase in recall values during inference by an average of 2.77X (with the highest increase as YOLOv3 by 6.5X). For performance, over both Original and Augmented datasets, the model training time reduces by an average of 4.45X when using A100 over Tesla T4.
This work was supported in part by the NSF research grants CCF #2132049, EEC #1941529, and a COR grant from University of California, Merced.
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Ng, D. et al. (2023). Benchmarking Object Detection Models with Mummy Nuts Datasets. In: Gainaru, A., Zhang, C., Luo, C. (eds) Benchmarking, Measuring, and Optimizing. Bench 2022. Lecture Notes in Computer Science, vol 13852. Springer, Cham. https://doi.org/10.1007/978-3-031-31180-2_7
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