We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| DetectionTeamUCAS/R2CNN_Faster-RCNN_Tensorflow | reimplementation | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| get_file_paths_recursive | Ran | DetectionTeamUCAS/R2CNN_Faster-RCNN_Tensorflow/tools/demo_rh.py code served (permissive licence) · get_code("98a1b9cf7e56c186") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Ship detection is of great importance and full of challenges in the field of remote sensing. The complexity of application scenarios, the redundancy of detection region, and the difficulty of dense ship detection are all the main obstacles that limit the successful operation of traditional methods in ship detection. In this paper, we propose a brand new detection model based on multitask rotational region convolutional neural network to solve the problems above. This model is mainly consist of five consecutive parts: Dense Feature Pyramid Network (DFPN), adaptive region of interest (ROI) Align, rotational bounding box regression, prow direction prediction and rotational nonmaximum suppression (R-NMS). First of all, the low-level location information and high-level semantic information are fully utilized through multiscale feature networks. Then, we design Adaptive ROI Align to obtain high quality proposals which remain complete spatial and semantic information. Unlike most previous approaches, the prediction obtained by our method is the minimum bounding rectangle of the object with less redundant regions. Therefore, rotational region detection framework is more suitable to detect the dense object than traditional detection model. Additionally, we can find the berthing and sailing direction of ship through prediction. A detailed evaluation based on SRSS for rotation detection shows that our detection method has a competitive performance.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1806.04828")
get_code_for_paper("1806.04828")
have("1806.04828")
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