Comparative Analysis of Machine Learning Models for Real-Time Object Detection - Wittenhagen, Merlin (Journal of Computer and Forensic Sciences, 2025)
: Object detection is a fundamental task in computer vision with applications ranging from autonomous driving, industrial automation and medical imaging. This report presents a comparative analysis of six well-known object detection models, precisely three small models for edge computing and three large models likely more suited for usage on high-performance systems. The models YOLOv10-Nano, MobileNetV3-SSDLite, EfficientDet-D0, Faster R-CNN, YOLOv10-Large and DETR were evaluated and compared based on their performance in terms of inference speed, accuracy and computational efficiency. The evaluation is conducted through both literature-based benchmarks and empirical tests on two different 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 significantly lower inference times. The 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. The findings highlight the importance of choosing the right model and appropriate hardware for specific application scenarios.
Keywords: Object Detection; Model Benchmarking; Inference Efficiency, Hardware-Aware Evaluation