Comparative analysis of machine learning models for real-time object detection

Object detection is a fundamental task in computer vision with applications ranging from autonomous driving to industrial automation and medical imaging. is report presents a comparative analysis of six well-known object detection models: three small models for edge computing and three large models likely more suited for usage on high-performance systems. e models YOLOv10-Nano, MobileNetV3-SSDLite, E cientDet-D0, Faster R-CNN, YOLOv10-Large, and DETR were evaluated and compared based on their performance in terms of inference speed, accuracy, and computational e ciency. e evaluation is conducted through both literature-based benchmarks and empirical tests on two di erent systems: an Apple Silicon M1 Pro-based system and an NVIDIA RTX 3080Ti-powered computer. Results show that YOLOv10 models consistently outperform the other models in real-time object detection as well as achieving superior accuracy in general while maintaining signi cantly lower inference times. e analysis further highlights compatibility issues with certain hardware, particularly focusing on PyTorch's MPS backend on Apple Silicon, which leads to serious performance drops in some models. e ndings highlight the importance of choosing the right model and appropriate hardware for speci c application scenarios.