Multi-Scale Target Detection Algorithm Based on Attention Mechanism

基于串联(concat)操作的特征融合方法仅仅融合了相邻尺度的特征,并没有充分利用来自其他尺度的输出特征。并且,串联操作只是在通道维度上将不同尺度的特征连接,不能反映不同通道间特征的相关性和重要性。针对这些问题,提出了一种基于注意力机制的特征融合算法。该算法利用注意力机制来融合不同尺度的特征,通过对每个通道的特征进行权重分配来学习不同通道间特征的相关性。将基于注意力机制的特征融合算法与YOLO V3相结合,构建多尺度目标检测器,并利用Focal loss和GIOU loss来设计检测器的损失函数。在PASCAL VOC和KITTI数据集上对不同算法进行对比实验,实验结果表明,多尺度目标检测器具有更高的检测精度和较快的检测速度。</p

A real-time small target detection network

Target detection based on deep convolutional neural network has achieved excellent performance. However, small target detection is still one of the challenges in the field of computer vision. In this paper, we present an efficient network for real-time small target detection. The proposed network performs feature extraction using a modified Darknet53, while utilizing scale matching strategy to select suitable scales and anchor size for small target detection. In the network, we design an adaptive receptive field fusion module to increase the context information around the small targets by merging the features with different receptive field. Furthermore, we also propose an image cropping method in data preprocessing, aiming to make the targets trained in a wider range of scales. We conduct experiments on VEDAI dataset and small target dataset. Comparative results show that the proposed network achieved 74.5% mean average precision (mAP) at 50.0 FPS on VEDAI dataset and 45.7% mAP at 51.1 FPS on small target dataset which is better than other advanced target detectors.</p

A Simple and Efficient Network for Small Target Detection

Target detection based on deep learning is developing rapidly. However, small target detection is still a challenge. In this paper, a simple and efficient network for small target detection is proposed. We put forward to improve the detection performance of the small targets in three aspects. First, as the contextual information is important to detect the small targets, we proposed to use &#39;dilated module&#39; to expand the receptive field without loss of resolution or coverage. Second, we applied feature fusion in different dilated modules to improve the ability of the network in detecting small targets. Finally, we used &#39;passthrough module&#39; to get the finer-grained information from the earlier layer and combined it with the semantic information from the deeper layer. To improve the detection speed of the network, it is proposed to use $1\times 1$ convolution to reduce the dimension of the network. We composed small vehicle dataset based on VEDAI dataset and DOTA dataset, respectively, and also analyzed the distribution of the small targets in each dataset. To evaluate the performance of the proposed network, we trained the model on the dataset above and compared with the state-of-the-art target detection algorithms, our approach achieved 80.16% average precision (AP) on VEDAI dataset and 88.63% AP on DOTA dataset and the frames per second (FPS) is 75.4. The AP of our network is much better than the result of the tiny YOLO V3 and is nearly the same as the result of the YOLO V3. However, the FPS of our network is almost the same as that of the tiny YOLO V3.</p

Brain-Inspired Fast Saliency-Based Filtering Algorithm for Ship Detection in High-Resolution SAR Images

In this article, we aim to improve the performance of synthetic aperture radar (SAR) ship detection under complex conditions. The complex backgrounds are commonly encountered for high-resolution (HR) SAR ship detection data set, and they greatly influence the detection performance of ships. In recent years, deep neural networks (DNNs) have made substantial improvements on detection by adopting data augmentation. However, the improvement is limited since the models are sensitive to noise. To address this problem, a Fast Saliency-based Filtering algorithm (FSF) is proposed to filter out interference information. The FSF method is inspired by the filtering mechanisms of the human brain, which help people filter out target-irrelevant information fast to better extract target-relevant information. The FSF includes two parts of the bottom-up process and the top-down process. The bottom-up process is used to extract a saliency map of an input image, and the other one is used to filter out target-irrelevant information based on the saliency map. The FSF can be a front-end preprocessing module of DNNs to fast filter out target-irrelevant information and obtain a primary priority map of an input image. Experimental results demonstrate that our brain-inspired FSF method obtains obvious improvement of detection performance on AIR-SARShip-1.0.</p

A Novel Evolution Strategy of Level Set Method for the Segmentation of Overlapping Cervical Cells

Development of an accurate and automated algorithm to completely segment cervical cells in Pap images is still one of the most challenging tasks. The main reasons are the presence of overlapping cells and the lack of guiding mechanism for the convergence of ill-defined contours to the actual cytoplasm boundaries. In this paper, we propose a novel method to address these problems based on level set method (LSM). Firstly, we proposed a morphological scaling-based topology filter (MSTF) and derived a new mathematical toolbox about vector calculus for evolution of level set function (LSF). Secondly, we combine MSTF and the mathematical toolbox into a multifunctional filtering algorithm 2D codimension two-object level set method (DCTLSM) to split touching cells. The DCTLSM can morphologically scale up and down the contour while keeping part of the contour points fixed. Thirdly, we design a contour scanning strategy as the evolution method of LSF to segment overlapping cells. In this strategy, a cutting line can be detected by morphologically scaling the union LSF of the pairs of cells. Then, we used this cutting line to construct a velocity field with an effective guiding mechanism for attracting and repelling LSF. The performance of the proposed algorithm was evaluated quantitatively and qualitatively on the ISBI-2014 dataset. The experimental results demonstrated that the proposed method is capable of fully segmenting cervical cells with superior segmentation accuracy compared with recent peer works.</p