Automated Object Detection and Count Estimation Based on Machine Learning Models

Abstract

Object detection and counting is a crucial problem in various areas like autonomous machines, health sector and industrial automation. This paper presents a comparative study between two Machine Learning (ML) methods, depending on Single Shot Multi Box Detector (SSD) with You Only Look Once (YOLOv3) and MobileNetv3 to focus on the object detection and counting. The well kown dataset (COCO) used to evaluate the two models where 44 images chosen randomly for testing. In addition SSD and MobileNet v3 compared in a real time mode. The performance metrics used in this paper were confidence scores, average over image and processing time. The results indicate that there was an tradeoff between accuracy and speed. Both models showed a fast inference time, which made it suitable for real-time applications, although (YOLOv3) was more confident in some cases because of its complex architecture. The performance difference is caused by the design of the model: MobileNet's simple structure aims at lightweight, while (YOLOv3)'s complex network enhance robustness in detection. This work highlights The necessity for application-aware model selection balancing between the fast performance on edge devices (such as drones) versus accuracy for accuracy-precision applications (like medical imaging). The findings provide practical insights for deploying ML-driven object detectors across various domain.