Search

Abstract This paper proposes an object detection model named Feature Enriching Object Detection Framework with Weak Segmentation Loss (FEOD) based on convolutional neural networks, which is an improvement of You Only Look Once (YOLO). To overcome the shortcoming of insensitivity to small objects, a novel feature enriching module is proposed to augment the semantic information of the feature in the shallow layer within a typical deep detector. Meanwhile, Focal Loss is also introduced to our model to further improve the algorithm performance. To obtain features more suitable for object detection, a more powerful feature extractor – DetNet is used as the backbone. An mAP of 83.8 in VOC2007 test and an mAP of 82.1 in VOC2012 test are achieved with a FPS of 11.3 with a NVIDIA GTX1080Ti GPU. For a lower resolution version, we achieve an mAP of 81.9 and 80.9 respectively in VOC2007 and VOC2012 tests with FPS of 17. Comparisons with other object detection algorithms have shown that our method works well and achieves a trade-off of accuracy and speed. [ABSTRACT FROM AUTHOR]

In this work, we propose a novel latent fingerprint enhancement method based on FingerNet inspired by recent development of Convolutional Neural Network (CNN). Although CNN is achieving superior performance in many computer vision tasks from low-level image processing to high-level semantic understanding, limited attention has been paid in fingerprint community. The proposed FingerNet has three major parts: one common convolution part shared by two different deconvolution parts, which are the enhancement branch and the orientation branch. The convolution part is to extract fingerprint features particularly for enhancement purpose. The enhancement deconvolution branch is employed to remove structured noise and enhance fingerprints as its task. The orientation deconvolution branch performs the task of guiding enhancement through a multi-task learning strategy. The network is trained in the manner of pixels-to-pixels and end-to-end learning, that can directly enhance latent fingerprint as the output. We also study some implementation details such as single-task learning, multi-task learning, and the residual learning. Experimental results of the FingerNet system on latent fingerprint dataset NIST SD27 demonstrate effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

• Recent studies show the potential of deep learning for binary classification of skin lesions. • This research proposes an advanced deep convolution neural network model for multi-class classification of skin lesions. • The proposed model is designed with several layers, and multiple filter sizes, but fewer filters and parameters. • It is evaluated by the experimental results on the ISIC-19 database in terms of sensitivity, specificity, and other metrics. • It outperforms state-of-the-art algorithms, exhibiting 0.991 AUROC. As an analytic tool in medicine, deep learning has gained great attention and opened new ways for disease diagnosis. Recent studies validate the effectiveness of deep learning algorithms for binary classification of skin lesions (i.e., melanomas and nevi classes) with dermoscopic images. Nonetheless, those binary classification methods cannot be applied to the general clinical situation of skin cancer screening in which multi-class classification must be taken into account. The main objective of this research is to develop, implement, and calibrate an advanced deep learning model in the context of automated multi-class classification of skin lesions. The proposed Deep Convolutional Neural Network (DCNN) model is carefully designed with several layers, and multiple filter sizes, but fewer filters and parameters to improve efficacy and performance. Dermoscopic images are acquired from the International Skin Imaging Collaboration databases (ISIC-17, ISIC-18, and ISIC-19) for experiments. The experimental results of the proposed DCNN approach are presented in terms of precision, sensitivity, specificity, and other metrics. Specifically, it attains 94 % precision, 93 % sensitivity, and 91 % specificity in ISIC-17. It is demonstrated by the experimental results that this proposed DCNN approach outperforms state-of-the-art algorithms, exhibiting 0.964 area under the receiver operating characteristics (AUROC) in ISIC-17 for the classification of skin lesions and can be used to assist dermatologists in classifying skin lesions. As a result, this proposed approach provides a novel and feasible way for automating and expediting the skin lesion classification task as well as saving effort, time, and human life. [ABSTRACT FROM AUTHOR]

Manual processing of Ground Penetrating Radar (GPR) images is a very time-intensive task. The authors proposed a novel computer vision-based method for automatic detection of rebars in complex GPR images in highly deteriorated concrete bridge decks. The proposed detection model consists of a fine-tuned Histogram of Oriented Gradients feature descriptor, a Multi-Layer Perceptron for classification, and a post processing algorithm for eliminating false detections and labeling rebar in Region of Interest. State-of-art results are obtained on testing the method on real bridge deck GPR data and comparing the results with RADAN software. Overall accuracy of 89.4% is obtained on URIGPRv1.0 dataset, which is introduced in this paper. The proposed method is 54.35% more accurate comparing to the results obtained by RADAN software. The proposed classifier outperformed accuracy of a 3-layer convolutional neural network by 11.9%. • Proposing a novel detection chain yields a new state-of-the-art result in rebar detection in GPR B-scan images • Optimizing HOG/LBPH feature computation and MLP model parameters for maximizing F-Measure in classification task • Developing a post-processing algorithm for minimizing false RoIs, removing non-top layer RoI, and extracting coordinates • Introducing a GPR dataset with 8,000 records, named URIGPRv1.0, cropped from real concrete bridge deck GPR B-scan images. • A computer program, named URICAB, is developed for automating interpretation of GPR data [ABSTRACT FROM AUTHOR]