One fundamental problem in Earth Vision is to accurately find the locations and identify the categories of the interesting objects in the aerial images, for which oriented bounding boxes (OBBs) are usually employed to depict better the objects emerging with arbitrary orientations. However, the regression of the OBBs always suffers from the ambiguous problem in the definition of the regression targets, which often reduces the convergency efficiency and decreases the detection accuracy. Although there are some methods like the binary segmentation map that can handle this problem, it brings a new problem of ambiguous background pixels in the OBBs. In this article, we propose to cast the OBB regression as a center-probability-map (CenterMap)-prediction problem, thus largely eliminating the ambiguities on the target definitions and the background pixels. The predicted CenterMaps are then used to generate the OBBs. The CenterMap OBB representation is simple, yet effective. Furthermore, to distinguish better the interesting objects from the cluttered background, a weighted pseudosegmentation-guided attention network is adopted to provide the object-level features for predicting the horizontal bounding boxes and the OBBs. The experimental results on three widely used data sets, i.e., DOTA, HRSC2016, and UCAS-AOD, demonstrate the effectiveness of our proposed method.

The constant Q transform (CQT) has been shown to be one of the most effective speech signal pre-transforms to facilitate synthetic speech detection, followed by either hand-crafted (subband) constant Q cepstral coefficient (CQCC) feature extraction and a back-end binary classifier, or a deep neural network (DNN) directly for further feature extraction and classification. Despite the rich literature on such a pipeline, we show in this paper that the pre-transform and hand-crafted features could simply be replaced by end-to-end DNNs. Specifically, we experimentally verify that by only using standard components, a light-weight neural network could outperform the state-of-the-art methods for the ASVspoof2019 challenge. The proposed model is termed Time-domain Synthetic Speech Detection Net (TSSDNet), having ResNet- or Inception-style structures. We further demonstrate that the proposed models also have attractive generalization capability. Trained on ASVspoof2019, they could achieve promising detection performance when tested on disjoint ASVspoof2015, significantly better than the existing cross-dataset results. This paper reveals the great potential of end-to-end DNNs for synthetic speech detection, without hand-crafted features.

Daily acquisition of large amounts of aerial and satellite images has facilitated subsequent automatic interpretations of these images. One such interpretation is object detection. Despite the great progress made in this domain, the detection of multi-scale objects, especially small objects in high resolution satellite (HRS) images, has not been adequately explored. As a result, the detection performance turns out to be poor. To address this problem, we first propose a unified multi-scale convolutional neural network (CNN) for geospatial object detection in HRS images. It consists of a multi-scale object proposal network and a multi-scale object detection network, both of which share a multi-scale base network. The base network can produce feature maps with different receptive fields to be responsible for objects with different scales. Then, we use the multi-scale object proposal network to generate high quality object proposals from the feature maps. Finally, we use these object proposals with the multi-scale object detection network to train a good object detector. Comprehensive evaluations on a publicly available remote sensing object detection dataset and comparisons with several state-of-the-art approaches demonstrate the effectiveness of the presented method. The proposed method achieves the best mean average precision (mAP) value of 89.6%, runs at 10 frames per second (FPS) on a GTX 1080Ti GPU.

Power line detection plays an important role in an automated UAV-based electricity inspection system, which is crucial for real-time motion planning and navigation along power lines. Previous methods which adopt traditional filters and gradients may fail to capture complete power lines due to noisy backgrounds. To overcome this, we develop an accurate power line detection method using convolutional and structured features. Specifically, we first build a convolutional neural network to obtain hierarchical responses from each layer. Simultaneously, the rich feature maps are integrated to produce a fusion output, then we extract the structured information including length, width, orientation and area from the coarsest feature map. Finally, we combine the fusion output with structured information to get a result with clear background. The proposed method fully exploits multiscale and structured prior information to conduct both accurate and efficient detection. In addition, we release two power line datasets due to the scarcity in the public domain. The method is evaluated on the well-annotated power line datasets and achieves competitive performance compared with state-of-the-art methods.

The robust detection of ships is one of the key techniques in coastal and marine applications of synthetic aperture radar (SAR). Conventional SAR ship detectors involved multiple parameters, which need to be estimated or determined very carefully. In this paper, we propose a new ship detection approach based on multi-scale heterogeneities under the a contrario decision framework, with a few parameters that can be easily determined. First, multi-scale heterogeneity features are extracted and fused to build a heterogeneity map, in which ships are well highlighted from backgrounds. Second, a set of reference objects are automatically selected by analyzing the saliency of local regions in the heterogeneity map and then are used to construct a null hypothesis model for the final decision. Finally, the detection results are obtained by using an a contrario decision. Experimental results on real SAR images demonstrate that the proposed method not only works more stably for ships with different sizes, but also has better performance than conventional ship detectors.

In electric network frequency (ENF) based audio forensics, the ENF signal captured in a questioned audio recording is estimated and analyzed for authentication purposes. However, the captured ENF signal is usually contaminated by very strong noise and interference. In this paper, we propose a robust filtering algorithm (RFA) for ENF signal enhancement in audio recordings, which could effectively suppress the additive noise and facilitate subsequent ENF estimation, especially in practical low signal-to-noise ratio (SNR) situations. The proposed algorithm encodes the time domain expression of the preprocessed audio signal (ENF signal plus noise) as the instantaneous frequencies (IFs) of an analytical sinusoidal frequency modulated (SFM) signal. Then, a kernel function is utilized to generate a sinusoidal time-frequency distribution (STFD) whose peaks correspond to the IFs of the analytical signal, i.e., the denoised ENF signal. It is then proven that finding the STFD peaks is equivalent to finding the averaged phases of the kernel function if the additive noise is a zero mean wide sense stationary (WSS) process. The RFA serves as a noise reduction mechanism yielding improved SNR at the filter output. Combined with generic frequency estimation methods, ENF extraction accuracy could be substantially improved with the use of the proposed RFA than without using it. Both synthetic and experimental results are provided to illustrate the effectiveness of our proposal. Reliable ENF extraction could be achieved under noise level down to -20 dB SNR, and the RFA is suitable for a wide range of ENF-based forensic applications.