Efficient Object Localization Scheme Based on Vanishing Line in Road Image for Autonomous Vehicles

This paper proposes an efficient object localization method based on a vanishing line. The proposed method can be much improved in time efficiency since it requires scanning only vanishing line area. It requires the time complexity of O(n) while the existing sliding window method requires the time complexity O(n2) for detecting all objects in the entire image. In addition, the range of detection area can be also remarkably reduced when compared with the sliding window method. As a result, the total range and times for searching in the proposed method can be significantly reduced by considering together the distance and position of the object. The experiment on the proposed method is performed with the virtual road data set known as SYNTHIA, and the competitive results are obtained.

Weakly Supervised Object Localization with Background Suppression Erasing for Art Authentication and Copyright Protection

The problem of art forgery and infringement is becoming increasingly prominent,since diverse self-media contents with all kinds of art pieces are released on the Internet every day.For art paintings,object detection and localization provide an efficient and ef-fective means of art authentication and copyright protection.However,the acquisition of a precise detector requires large amounts of ex-pensive pixel-level annotations.To alleviate this,we propose a novel weakly supervised object localization(WSOL)with background su-perposition erasing(BSE),which recognizes objects with inexpensive image-level labels.First,integrated adversarial erasing(IAE)for vanilla convolutional neural network(CNN)dropouts the most discriminative region by leveraging high-level semantic information.Second,a background suppression module(BSM)limits the activation area of the IAE to the object region through a self-guidance mechanism.Finally,in the inference phase,we utilize the refined importance map(RIM)of middle features to obtain class-agnostic loc-alization results.Extensive experiments are conducted on paintings,CUB-200-2011 and ILSVRC to validate the effectiveness of our BSE.

Distributed environment representation and object localization system in intelligent space

A kind of new environment representation and object localization scheme is proposed in the paper aiming to accomplish the task of object operation more efficiently in intelligent space. First, a distributed environment represen- tation method is put forward to reduce storage burden and improve the system＇s stability. The layered topological maps are separately stored in different landmarks attached to the key positions of intelligent space, so that the robot can search the landmarks on which the map information can be read from the QR code, and then the environment map can be built autonomously. Map building is an important prerequisite for object search. An object search scheme based on RFID and vision technology is proposed. The RFID tags are attached to the target objects and reference objects in the indoor environ- ment. A fixed RFID system is built to monitor the rough position （room and local area） of target and a mobile RFID system is constructed to detect the targets which are not in the covering range of the fixed system. The existing area of target is determined by the time sequence of reference tags and target tags, and the accurate position is obtained by onboard vision system at a short distance. The experiments demonstrate that the distributed environment representation proposed in the paper can fully meet the requirements of object localization, and the positioning scheme has high search efficiency, high localization accuracy and precision, and a strong anti-interference ability in the complex indoor environment.

A survey: which features are required for dynamic visual simultaneous localization and mapping?

In recent years,simultaneous localization and mapping in dynamic environments(dynamic SLAM)has attracted significant attention from both academia and industry.Some pioneering work on this technique has expanded the potential of robotic applications.Compared to standard SLAM under the static world assumption,dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly.Therefore,dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments.Additionally,to meet the demands of some high-level tasks,dynamic SLAM can be integrated with multiple object tracking.This article presents a survey on dynamic SLAM from the perspective of feature choices.A discussion of the advantages and disadvantages of different visual features is provided in this article.

Compressive sensing for small moving space object detection in astronomical images

It is known that detecting small moving objects in as- tronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, pro- vides a very easy and computationally cheap coding scheme for onboard astronomical remote sensing. An algorithm for small moving space object detection and localization is proposed. The algorithm determines the measurements of objects by comparing the difference between the measurements of the current image and the measurements of the background scene. In contrast to reconstruct the whole image, only a foreground image is recon- structed, which will lead to an effective computational performance, and a high level of localization accuracy is achieved. Experiments and analysis are provided to show the performance of the pro- posed approach on detection and localization.

TWO-STAGE OCCLUDED OBJECT RECOGNITION METHOD FOR MICROASSEMBLY

A two-stage object recognition algorithm with the presence of occlusion is presented for microassembly. Coarse localization determines whether template is in image or not and approximately where it is, and fine localization gives its accurate position. In coarse localization, local feature, which is invariant to translation, rotation and occlusion, is used to form signatures. By comparing signature of template with that of image, approximate transformation parameter from template to image is obtained, which is used as initial parameter value for fine localization. An objective function, which is a function of transformation parameter, is constructed in fine localization and minimized to realize sub-pixel localization accuracy. The occluded pixels are not taken into account in objective function, so the localization accuracy will not be influenced by the occlusion.

A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network

This paper proposes a cascade deep convolutional neural network to address the loosening detection problem of bolts on axlebox covers.Firstly,an SSD network based on ResNet50 and CBAM module by improving bolt image features is proposed for locating bolts on axlebox covers.And then,theA2-PFN is proposed according to the slender features of the marker lines for extracting more accurate marker lines regions of the bolts.Finally,a rectangular approximationmethod is proposed to regularize themarker line regions asaway tocalculate the angle of themarker line and plot all the angle values into an angle table,according to which the criteria of the angle table can determine whether the bolt with the marker line is in danger of loosening.Meanwhile,our improved algorithm is compared with the pre-improved algorithmin the object localization stage.The results show that our proposed method has a significant improvement in both detection accuracy and detection speed,where ourmAP(IoU=0.75)reaches 0.77 and fps reaches 16.6.And in the saliency detection stage,after qualitative comparison and quantitative comparison,our method significantly outperforms other state-of-the-art methods,where our MAE reaches 0.092,F-measure reaches 0.948 and AUC reaches 0.943.Ultimately,according to the angle table,out of 676 bolt samples,a total of 60 bolts are loose,69 bolts are at risk of loosening,and 547 bolts are tightened.

Image-Based Automatic Energy Meter Reading Using Deep Learning

We propose to perform an image-based framework for electrical energy meter reading.Our aim is to extract the image region that depicts the digits and then recognize them to record the consumed units.Combining the readings of serial numbers and energy meter units,an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time setup.However,such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display,orientation and scale variations caused by camera positioning,and non-uniform illumination caused by shades.To this end,our work evaluates and compares the stateof-the art deep learning algorithm You Only Look Once(YOLO)along with traditional handcrafted features for text extraction and recognition.Our image dataset contains 10,000 images of electrical energymeters and is further expanded by data augmentation such as in-plane rotation and scaling tomake the deep learning algorithms robust to these image variations.For training and evaluation,the image dataset is annotated to produce the ground truth of all the images.Consequently,YOLO achieves superior performance over the traditional handcrafted features with an average recognition rate of 98%for all the digits.It proves to be robust against the mentioned image variations compared with the traditional handcrafted features.Our proposed method can be highly instrumental in reducing the time and effort involved in the currentmeter reading,where workers visit door to door,take images ofmeters and manually extract readings from these images.

Shallow Feature-driven Dual-edges Localization Network for Weakly Supervised Localization

Weakly supervised object localization mines the pixel-level location information based on image-level annotations.The traditional weakly supervised object localization approaches exploit the last convolutional feature map to locate the discriminative regions with abundant semantics.Although it shows the localization ability of classification network,the process lacks the use of shallow edge and texture features,which cannot meet the requirement of object integrity in the localization task.Thus,we propose a novel shallow feature-driven dual-edges localization(DEL)network,in which dual kinds of shallow edges are utilized to mine entire target object regions.Specifically,we design an edge feature mining(EFM)module to extract the shallow edge details through the similarity measurement between the original class activation map and shallow features.We exploit the EFM module to extract two kinds of edges,named the edge of the shallow feature map and the edge of shallow gradients,for enhancing the edge details of the target object in the last convolutional feature map.The total process is proposed during the inference stage,which does not bring extra training costs.Extensive experiments on both the ILSVRC and CUB-200-2011 datasets show that the DEL method obtains consistency and substantial performance improvements compared with the existing methods.

Method for Visual Localization of Oil and Gas Wellhead Based on Distance Function of Projected Features

A localization method based on distance function of projected features is presented to solve the accuracy reduction or failure problem due to occlusion and blurring caused by smog, when dealing with vision based localization for target oil and gas wellhead （OGWH）. Firstly, the target OGWH is modeled as a cylinder with marker, and a vector with redundant parameter is used to describe its pose. Secondly, the explicit mapping relationship between the pose vector with redundant parameter and projected features is derived. Then, a 2D-point-to-feature distance function is proposed, as well as its derivative. Finally, based on this distance function and its derivative, an algorithm is proposed to estimate the pose of target OGWH directly according to the 2D image information, and the validity of the method is verified by both synthetic data and real image experiments. The results show that this method is able to accomplish the localization in the case of occlusion and blurring, and its anti-noise ability is good especially with noise ratio of less than 70%.

UAV image target localization method based on outlier filter and frame buffer

With rapid development of UAV technology,research on UAV image analysis has gained attention.As the existing techniques of UAV target localization often rely on additional equipment,a method of UAV target localization based on depth estimation has been proposed.However,the unique perspective of UAVs poses challenges such as the significant field of view variations and the presence of dynamic objects in the scene.As a result,the existing methods of depth estimation and scale recovery cannot be directly applied to UAV perspectives.Additionally,there is a scarcity of depth estimation datasets tailored for UAV perspectives,which makes supervised algorithms impractical.To address these issues,an outlier filter is introduced to enhance the applicability of depth estimation networks to target localization.A frame buffer method is proposed to achieve more accurate scale recovery,so as to handle complex scene textures in UAV images.The proposed method demonstrates a 14.29%improvement over the baseline.Compared with the average recovery results from UAV perspectives,the difference is only 0.88%,approaching the performance of scale recovery using ground truth labels.Furthermore,to overcome the limited availability of traditional UAV depth datasets,a method for generating depth labels from video sequences is proposed.Compared to state-of-the-art methods,the proposed approach achieves higher accuracy in depth estimation and stands for the first attempt at target localization using image sequences.Proposed algorithm and dataset are available at https://github.com/uav-tan/uav-object-localization.

Lateral line system of fish

The lateral line is a sensory system that allows fishes to detect weak water motions and pressure gradients.The smallest functional unit of the lateral line is the neuromast,a sensory structure that consists of a hair cell epithelium and a cupula that connects the ciliary bundles of the hair cells with the water surrounding the fish.The lateral line of most fishes consists of hundreds of superficial neuromasts spread over the head,trunk and tail fin.In addition,many fish have neuromasts embedded in lateral line canals that open to the environment through a series of pores.The present paper reviews some more recent aspects of the morphology,behavioral relevance and physiology of the fish lateral line.In addition,it reports some new findings with regard to the coding of bulk water flow.