<![CDATA[The evolution of object identification technologies, particularly for person detection applications, has increasingly accelerated due to the merger of deep learning and artificial intelligence with computer vision. This study intends to test the efficacy of two object detection algorithms, YOLOv8n and CNN MobileNetSSD, in identifying human objects in digital photos. A dataset of 12,334 human-labeled photos from the Roboflow platform was utilized to train the YOLOv8n model, while performance results for the CNN MobileNetSSD model were acquired from a prior article. The precision, recall, and F1-score of each model were examined. Experimental results reveal that YOLOv8n attains 94% precision, 92% recall, and a 92.9% F1-score, representing a considerable enhancement over MobileNetSSD. Conversely, MobileNetSSD got an F1-score of 85.2%, with a precision of 86.5% and a recall of 84.1%. The findings show that CNN MobileNetSSD is more ideal for non-time-sensitive or resource-limited scenarios; however, YOLOv8n is preferable for real-time human identification tasks due to its greater accuracy and faster inference. This comparative analysis is important for differentiating object detection models matched to certain application needs.]]>
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