Context. Underwater human detection has been an important problem in computer vision areas. Body part-based models could gain good performance in on-land human detection with occlusion existing scenarios. This thesis explores the feasibility of human body parts detection in underwater environment. Objectives. This thesis aims to build a DNN-based underwater human body part detector for human body part detection task. Three body part detectors implemented with different DNN-based models (Faster R-CNN, SSD and YOLO) are built and compared over underwater human body part detection task. Methods. In this thesis, experiments are used as research methods. Three DNN-based models which are regarded as the independent variables in the experiment is trained, tested and evaluated. And the detection results of detector based on the three different models are dependent variables. Finally the detection performance calculated on the result for each detector is compared. Results. Underwater Body part detector based on Faster R-CNN provides the best detection performance on the body part detection task in terms of mAP, and YOLOv2 achieves the fastest detection speed but it has the smallest mAP value. In addition, SSD model has both decent detection performance and also detection speed. Conclusions. Underwater Body part detector based on Faster R-CNN, SSD, and YOLO could gain good performance over underwater human body part detection task. Building an underwater body part detector via deep learning method is feasible.