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In this paper, we propose an adaptive obstacle avoidance algorithm based on DDPG (Deep Deterministic Policy Gradient) and DWA (Dynamic-Window Approach) to study the obstacle avoidance problem of robots in complex continuous state space. First, the obstacle avoidance problem is converted into an optimal learning incentive problem, and the self-learning of the obstacle avoidance policy is realized based on DDPG; second, the DWA obstacle avoidance trajectory evaluation function is optimized using the DDPG reward incentive mechanism, and the Experience Replay mechanism; finally, the algorithm model is simulated. The experiments show that the model can significantly circumvent the deficiency of the DWA algorithm in limiting to the optimal local solution in a complex environment and solve the action output problem in the continuous velocity and turning angle value interval of the robot; through the trial and error interaction with the environment and the trajectory evaluation incentive feedback, the obstacle avoidance passing ability of the robot in a complex environment is improved. [Display omitted] • We propose an adaptive obstacle avoidance algorithm based on DDPG (Deep Deterministic Policy Gradient) and DWA (Dynamic Window Approach), named the DDPG-DWA. • The DDPG-DWA algorithm can solve the problem of action output in the continuous velocity and turning angle value range of the robot. • The DDPG-DWA algorithm can avoid the problem of DWA algorithm easily being limited to local obstacle avoidance optimal solutions in complex environments. • The model is suitable for path obstacle avoidance of intelligent agents in complex, narrow and irregular environments. [ABSTRACT FROM AUTHOR]

The stability of data transmission in the network can be maintained by a survivable spanning connection (SSC), which consists of two not-fully-disjoint spanning trees. Existing works restore the faulty SSC from link failures by regenerating two new not-fully-disjoint spanning trees. However, it takes plenty of recovery time, especially in a large network. This paper aims to fastly restore the faulty SSC by adjusting and updating its faulty spanning trees. A SSC restoration problem is formulated to generate a restored SSC with nearly optimal network survivability and desired bandwidth requirement. To solve the formulated problem, two fast restoration algorithms are proposed to cope with the shared link failure and the unshared link failure, respectively. Simulation results show that, the proposed algorithms can reduce the recovery time by 36.69% and 55.54%, respectively, compared with the existing algorithm. In addition, the proposed algorithms can achieve nearly optimal network survivability. [Display omitted] • A problem is proposed to restore survivable spanning connection with link failures. • Survivable spanning connection is restored by updating its faulty spanning trees. • Two algorithms are proposed for shared and unshared link failures, respectively. • The proposed algorithms can significantly reduce the recovery time. • The proposed algorithms can achieve nearly optimal network survivability. [ABSTRACT FROM AUTHOR]

The smart extraction of key frames is one of the critical technologies for vehicle target recognition in the video streaming environment. Currently, the common algorithms are scale-invariant feature transform (SIFT) or background-difference method. Both algorithms have their own advantages and shortcomings. In this paper, a smart key frame extraction algorithm is proposed by combining the background difference method and SIFT feature matching algorithm, at the same time, the criterion factor K is introduced. The algorithm absorbs the idea of background difference and SIFT feature matching, and divides video frames into mutation frames and non-mutation frames. Hash algorithm is used to distinguish the similarity of two frames and remove redundant frames. The local feature of video sequence is introduced to solve the problem of sharp increasing calculation for inter frame distance. Experimental results show that the improved smart key frame extraction algorithm has a lower missing frame rate and higher accuracy for vehicle target recognition. [ABSTRACT FROM AUTHOR]

As an essential subproblem in the face recognition field, occluded face recognition has received considerable attention in recent years. However, satisfactory recognition accuracy for occluded faces has not been achieved. Therefore, in order to solve this problem, this paper proposes a convolutional neural network that has multidimensional serial feature extraction modules for occluded faces and uses the deep learning method to improve the recognition rate. In order to improve the expression ability of the network, the method extracts features from two dimensions — space and channel — and built a multidimensional serial feature extraction module. By "multiscale" and "dependence" processing features, the generalization ability was significantly improved. Through the experimental test, the recognition accuracy of the multidimensional feature network (MFNet) reached 90.35%. This accuracy is 17.89% (72.36%) better than that of the traditional algorithm. Meanwhile, compared with other convolutional neural networks, this network has different degrees of improvement, which are 0.68% (ArcFace), 2.14% (Visual Geometry Group Face), and 4.83% (DeepID3). [ABSTRACT FROM AUTHOR]

• A cross-layer parallel attention network is proposed to further refine the semantic information of the high-level fusion features and the fine-grained information of the low-level fusion features. • A multiscale perception module is proposed to improve the robustness of the network to object scale change. • An effective anchor box allocation strategy is proposed to detect small objects at the low-level feature maps. • Our model can achieve mAP of 88.1% on the GDUT-HWD dataset and mAP of 80.5% on the PASCAL VOC 2007 dataset respectively. To solve the problem of low accuracy in existing safety helmet detection methods, a novel object detection algorithm based on Single Shot Multibox Detector (SSD) is proposed in this paper. The algorithm uses the spatial attention mechanism for low-level features and the channel attention mechanism for high-level features, this cross-layer attention mechanism can further refine the feature information of the object region. The proposed detection algorithm introduces a feature pyramid and multiscale perception module to improve its robustness to object scale change. In addition, an effective anchor box adaptive adjustment method is designed to adaptively adjust the scale distribution of each layer of the anchor boxes based on the feature map size. Experiment results demonstrate that our detection model has mean Average Precision (mAP) of 88.1% and 80.5% on helmet dataset and VOC 2007 dataset respectively, which is better than baseline by 15.65% and 3.4%. The network framework of CASP detection algorithm for safety helmet-wearing detection. [Display omitted] [ABSTRACT FROM AUTHOR]

In black-box scenarios, the adversarial attack algorithms based on iterative optimization perform best. But it may give a false sense of model robustness due to the design of inefficient queries. The adversarial attack algorithm based on multi-objective evolution optimization has been proven to be very effective for the low-dimensional images. However, when the attack space dramatically increases for the high-dimensional color images, the evolutionary efficiency is limited, and it needs more inefficient queries to generate adversarial examples. In this paper, we propose an efficient black-box adversarial attack approach for high dimensional images based on multi-objective optimization (MOO-HD), which includes some novel strategies to solve the above problems. We also propose the strategy of "The transformation of the pixel block with a random step size" to reduce the attack space. The experimental results on three image datasets with different dimensions show that our algorithm can achieve a higher success rate with fewer queries. [ABSTRACT FROM AUTHOR]