Currently we are using yolo v8 for our object detection model .we practiced to work it but it detects only for short range like ( 10 metre ) . That's the major issue we are facing now .is that any ways to increase the range for detection ? And need some optimization methods for box loss . Also is there any models that outperform yolo v8?
List of algorithms we currently used : yolo and ultralytics for detection (we annotated using roboflow ) ,nms for double boxing , kalman for tracking ,pygames for gui , cv2 for live feed from camera using RTSP . Camera (hikvision ds-2de4425iw-de )
