I have used some of the detectors (deep neural networks for object detection) like Mask-RCNN and YOLO pre-trained networks for some projects like waste-water bottle detection in a video and measuring speed and tracking boat in a boat race. For some other projects like recognition of water levels for flood prediction, I have used image segmentation R-CNN family of algorithms. We achieve pretty high accuracy (about 94%) in detecting real-flood events.There are many versions of these two detectors like Faster R-CNN and YOLOv5. RetinaNet is also a very efficient object detection algorithm currently in use.For object detection, each algorithm has its own pros and cons. For instance, the R-CNN family of object detection has a slower inference time than the YOLO family for real-time detection. However, when real-time detection is not a constraint, then the R-CNN family can give higher efficiency in detecting objects. There are also some libraries, like Detectron2, developed by the Facebook team, that support R-CNN-like algorithms for object detection.