Abstract
The ability to detect small targets and the speed of the target detector are very important for the application of remote sensing image detection, and in this paper, we propose an effective and efficient method (named CISPNet) with high detection accuracy and compact architecture. In particular, according to the characteristics of the data, we apply a context information scene perception (CISP) module to obtain the contextual information for targets of different scales and use k-means clustering to set the aspect ratios and size of the default boxes. The proposed method inherits the network structure of Single Shot MultiBox Detector (SSD) and introduces the CISP module into it. We create a dataset in the Pascal Visual Object Classes (VOC) format, annotated with the three types of detection targets, aircraft, ship, and oiltanker. Experimental results on our remote sensing image dataset as well as the Northwestern Polytechnical University very-high-resolution (NWPU VRH-10) dataset demonstrate that the proposed CISPNet performs much better than the original SSD and other detectors especially for small objects. Specifically, our network can achieve 80.34% mean average precision (mAP) at the speed of 50.7 frames per second (FPS) with the input size 300 × 300 pixels on the remote sensing image dataset. On extended experiments, the performance of CISPNet in fuzzy target detection in remote sensing image is better than that of SSD.
Highlights
With the rapid development of remote sensing spaceborne technologies, such as Sentinel-1, TerraSAR-X, and RADARSAT-2 [1,2,3], target detection on remote sensing images has been playing an important position in the field of civil areas and defense security [4,5,6]
To address the above problem, especially for small target detection, in this paper, we present a single-stage detector named context information scene perception (CISP)Net for target detection on single-stage detector named context information scene perception (CISP)Net for target detection on remote sensing images, which is based on the Single Shot MultiBox Detector (SSD) [27] and apply a remote sensing images, which is based on the Single Shot MultiBox Detector (SSD) [27] and apply a context information scene perception (CISP) module to obtain the context information for targets of context information scene perception (CISP) module to obtain the context information for targets of different scales
Some ablation studies are discussed to verify the role of each component, we first introduce the construction of the dataset for target detection in a remote sensing image, and illustrate the evaluation metrics, training strategies, and implementation details
Summary
With the rapid development of remote sensing spaceborne technologies, such as Sentinel-1, TerraSAR-X, and RADARSAT-2 [1,2,3], target detection on remote sensing images has been playing an important position in the field of civil areas and defense security [4,5,6]. To address the above problem, especially for small target detection, in this paper, we present a single-stage detector named context information scene perception (CISP)Net for target detection on single-stage detector named context information scene perception (CISP)Net for target detection on remote sensing images, which is based on the Single Shot MultiBox Detector (SSD) [27] and apply a remote sensing images, which is based on the Single Shot MultiBox Detector (SSD) [27] and apply a context information scene perception (CISP) module to obtain the context information for targets of context information scene perception (CISP) module to obtain the context information for targets of different scales. Compared with other detection methods such as YOLO [24], SSD [27], DSSD [28], RSSD [32], our framework is more suitable for target detection in remote sensing images, and has and RSSD [32], our framework is more suitable for target detection in remote sensing images, and achieved the relatively advanced performance.
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