Why YOLOv8 Architecture Excels in Handling Small Object Detection

[Luke Gray]

Detecting small objects has always been a weak spot for many real-time detection models. They often lose critical details during downsampling, leading to poor accuracy. The YOLOv8 architecture addresses this issue through an improved backbone and feature aggregation strategy. By utilizing enhanced CSP blocks and an efficient PAN-style neck, it preserves fine-grained information across multiple scales. This allows the model to maintain strong performance even when objects appear tiny or partially occluded in complex scenes. For industries like surveillance, aerial imaging, and autonomous navigation, this improvement is a game changer.