Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection

Multispectral pedestrian detection has received extensive attention in recent years as a promising solution to facilitate robust human target detection for around-the-clock applications (e.g. security surveillance and autonomous driving). In this paper, we demonstrate illumination information encoded in multispectral images can be utilized to significantly boost performance of pedestrian detection. A novel illumination-aware weighting mechanism is present to accurately depict illumination condition of a scene. Such illumination information is incorporated into two-stream deep convolutional neural networks to learn multispectral human-related features under different illumination conditions (daytime and nighttime). Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which are used to boost pedestrian detection accuracy. Putting all of the pieces together, we present a powerful framework for multispectral pedestrian detection based on multi-task learning of illumination-aware pedestrian detection and semantic segmentation. Our proposed method is trained end-to-end using a well-designed multi-task loss function and outperforms state-of-the-art approaches on KAIST multispectral pedestrian dataset

Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g. daytime and nighttime). In this paper, we present a novel box-level segmentation supervised learning framework for accurate and real-time multispectral pedestrian detection by incorporating features extracted in visible and infrared channels. Specifically, our method takes pairs of aligned visible and infrared images with easily obtained bounding box annotations as input and estimates accurate prediction maps to highlight the existence of pedestrians. It offers two major advantages over the existing anchor box based multispectral detection methods. Firstly, it overcomes the hyperparameter setting problem occurred during the training phase of anchor box based detectors and can obtain more accurate detection results, especially for small and occluded pedestrian instances. Secondly, it is capable of generating accurate detection results using small-size input images, leading to improvement of computational efficiency for real-time autonomous driving applications. Experimental results on KAIST multispectral dataset show that our proposed method outperforms state-of-the-art approaches in terms of both accuracy and speed

Unsupervised Domain Adaptation for Multispectral Pedestrian Detection

Multimodal information (e.g., visible and thermal) can generate robust pedestrian detections to facilitate around-the-clock computer vision applications, such as autonomous driving and video surveillance. However, it still remains a crucial challenge to train a reliable detector working well in different multispectral pedestrian datasets without manual annotations. In this paper, we propose a novel unsupervised domain adaptation framework for multispectral pedestrian detection, by iteratively generating pseudo annotations and updating the parameters of our designed multispectral pedestrian detector on target domain. Pseudo annotations are generated using the detector trained on source domain, and then updated by fixing the parameters of detector and minimizing the cross entropy loss without back-propagation. Training labels are generated using the pseudo annotations by considering the characteristics of similarity and complementarity between well-aligned visible and infrared image pairs. The parameters of detector are updated using the generated labels by minimizing our defined multi-detection loss function with back-propagation. The optimal parameters of detector can be obtained after iteratively updating the pseudo annotations and parameters. Experimental results show that our proposed unsupervised multimodal domain adaptation method achieves significantly higher detection performance than the approach without domain adaptation, and is competitive with the supervised multispectral pedestrian detectors

Study on Resource Configuration on Cloud Manufacturing

The purpose of manufacturing is to realize the requirement of customer. In manufacturing process of cloud system, there exist a lot of resource services which have similar functional characteristics to realize the requirement. It makes the manufacturing process more diverse. To develop the quality and reduce cost, a resource configuration model on cloud-manufacturing platform is put forward in this paper. According to the generalized six-point location principle, a growth design from the requirement of customers to entities with geometric constraints is proposed. By the requirement growing up to product, a configuration process is used to match the entities with the instances which the resources in the database could supply. Different from most existing studies, this paper studies the tolerance design with multiple candidate resource suppliers on cloud manufacturing to make the market play a two-level game considering the benefit of customers and the profit of resources to give an optimal result. A numerical case study is used to illustrate the proposed model and configuration process. The performance and advantage of the proposed method are discussed at the end

Learning Inter- and Intra-frame Representations for Non-Lambertian Photometric Stereo

In this paper, we build a two-stage Convolutional Neural Network (CNN) architecture to construct inter- and intra-frame representations based on an arbitrary number of images captured under different light directions, performing accurate normal estimation of non-Lambertian objects. We experimentally investigate numerous network design alternatives for identifying the optimal scheme to deploy inter-frame and intra-frame feature extraction modules for the photometric stereo problem. Moreover, we propose to utilize the easily obtained object mask for eliminating adverse interference from invalid background regions in intra-frame spatial convolutions, thus effectively improve the accuracy of normal estimation for surfaces made of dark materials or with cast shadows. Experimental results demonstrate that proposed masked two-stage photometric stereo CNN model (MT-PS-CNN) performs favorably against state-of-the-art photometric stereo techniques in terms of both accuracy and efficiency. In addition, the proposed method is capable of predicting accurate and rich surface normal details for non-Lambertian objects of complex geometry and performs stably given inputs captured in both sparse and dense lighting distributions.Comment: 9 pages,8 figure

PO-081 Overtraining results in abnormality of renal silt diaphragm in rats with persistent proteinuria

Objective Silt diaphragm is the most important and bioactive membrane structure in the filtration barrier of kidney, and the root cause of proteinuria is the structural and functional abnormalities of Silt diaphragm. So far, there is little literature on the changes of silt diaphragm caused by overtraining. This research establishes a model of rats with exercise-induced proteinuria with long-term intensitive treadmill exercise, and it simulates the progressive-load training in the cycle of athletes. Histological and ultrastructural changes of kidney immediately and 24 h after exercise are observed, and it aims to analyze the change law of silt diaphragm during the occurrence of persistent proteinuria. Methods this study selects 36 Sprague-Dawley rats, which are randomly divided into 3 groups: a control group (group C, 12), a group drawn immediately after exercise(group EI, 12), a group drawn 24 h after exercise(group EA, 12). Group C does not train. The rats in group EI and EA train on the treadmill with an increasing load for 6 weeks(10% grade, 6 d/w): in the first week, the rats run for 10 min at 10 m/min. Starting from the second week, the running speed increases by 5m/min/w, and the training time increases by 30min/w. In the last week the rats run to exhaustion if they could not maintain the target intensity. Record the exhausting time of rats, then group EI and group EA are respectively drawn immediately and 24 hours after exercise. Observe the histological changes of  renal glomerulus by optical microscope, and the ultrastructure of silt diaphragm by TEM. Detect urine total protein by BCA, serum and urine creatinine by Jaffe, serum testosterone and corticosterone by radioimmunoassay, serum urea by two-point dynamic method, and the expression of Nephrin by western-blot. Results The rats in group EI and EA gradually lose weight at the first weekend of training, and their weight drop significantly from the third weekend to the end, it shows a significant difference compared with group C(p<0.01). There is no significant difference between the exercise group. Glomerular morphology, group C: The structure of glomerulus is compact, the boundary between vascular sphere and  the wall of capsule is obvious, and the distribution of erythrocytes in vessels is regular; Group EI: The thickness of glomerulus membrane is uneven, the structure of the podocyte is incomplete, part of the foot process is fused, and SD is discontinuous; Group EA: Part of the glomerular endodermis is abnormal, part of the foot process is fused, detached, and unevenly distributed, and SD is discontinuous. Total protein/ creatinine in urine of rats 30 min and 24 h after exercise is significantly higher than that of group C(p<0.01), and group EA is slightly retuned and lower than group EI(p<0.05). Compared with group C, Serum Testosterone/Corticosterone of rats in group EI and EA is significantly decreased, and there is a significant difference (p<0.01). However, there is a significant decrease in EA group but it is still significantly lower than group EI(p<0.01). Serum corticosterone is significanty decreased in group EI and EA, and there is a significant difference (p<0.01), while group EA is significantly decreased but still significantly lower than that in group EI(p<0.01). Serum urea and espression of Nephrin are significantly decreased in group EI and EA(p<0.01), but there are no significant difference between group EI and EA(p>0.05). Conclusions The rats suffer from overtraining syndrome caused by intensitive training, and with persistent proteinuria, their renal function is disordered and cannot be effectively recovered after 24 h rest. Meanwhile, the renal morphology and ultrastructure of silt diaphragm in rats undergo "pathogenical-like" changes, which do not significantly decrease with the extension of recovery time. It is revealed that the injury of renal structure and ultrastructure of silt diaphragm caused by overtraining are the structural basis of continuous exercise-induced proteinuria

An investigation into methods for predicting material removal energy consumption in turning

The wide use of machining processes has imposed a large pressure on environment due to energy consumption and related carbon emissions. The total power required in machining include power consumed by the machine before it starts cutting and power consumed to remove material from workpiece. Accurate prediction of energy consumption in machining is the basis for energy reduction. This paper investigates the prediction accuracy of the material removal power in turning processes, which could vary a lot due to different methods used for prediction. Three methods, namely the specific energy based method, cutting force based method and exponential function based method are considered together with model coefficients obtained from literature and experiments. The methods have been applied to a cylindrical turning of three types of workpiece materials (carbon steel, aluminum and ductile iron). Methods with model coefficients obtained from experiments could achieve a higher prediction accuracy than those from literature, which can be explained by the inability of the coefficients from literature to match the specific machining conditions. When the coefficients are obtained from literature, the prediction accuracy is largely dependent on the sources of coefficients and there is no definitive dominance of one approach over another. With model coefficients from experiments, the cutting force based model achieves the best accuracy, followed by the exponential function based method and specific energy based method. Furthermore, the power prediction methods can be used in process design stage to support energy consumption reduction of a machining process