Name
Abstract | ||
|---|---|---|
Underwater coral reef fish detection is topic receiving increasingly attention due to its importance in various applications like fish biodiversity monitoring, marine resource managements, etc. However, compared with studies on generic object detection, existing methods on this task are not mature so far where advanced deep models and technologies are seldom considered. This paper presents FFDet, a fully convolutional network for coral reef fish detection by layer fusion. FFDet consists of a single shot multibox detector (SSD)-based backbone, but different with SSD, it devises a novel feature fusion module to aggregate adjacent prediction layers for enhanced feature representation. Thus, instead of using the prediction layers one-by-one, the enhanced features each combining information from multiple layers, are leveraged to detect fishes at different scales. We argue that the proposed module is capable of encoding both strong semantics and detail context information. Experimental results on SeaCLEF dataset show that FFDet not only outperforms SSD in performance by sacrificing only a little efficiency, but also better than another two popular end-to-end deep models in both detection performance and speed, especially on detecting large-sized fishes. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/VCIP.2018.8698738 | 2018 IEEE Visual Communications and Image Processing (VCIP) |
Keywords | Field | DocType |
Fully convolutional network,Fish detection,Feature layer fusion,Underwater video | Computer vision,Object detection,Feature fusion,Pattern recognition,Computer science,Fusion,Artificial intelligence,Coral reef fish,Detector,Semantics,Encoding (memory),Underwater | Conference |
ISBN | Citations | PageRank |
978-1-5386-4458-4 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cuncun Shi | 1 | 0 | 0.34 |
| Caiyan Jia | 2 | 81 | 13.07 |
| Zhineng Chen | 3 | 192 | 25.29 |