In autonomous driving community, numerous benchmarks have been established to assist the tasks of 3D/2D object detection, stereo vision, semantic/instance segmentation. However, more meaningful dynamic evolution surrounding objects ego-vehicle is rarely exploited, and lacks a large-scale dataset platform. To address this, we introduce BLVD, 5D semantics benchmark which does not concentrate on static detection or segmentation tackled adequately before. Instead, BLVD aims provide platform for...

Semantic image segmentation, which aims at assigning pixel-wise category, is one of challenging understanding problems. Global context plays an important role on local category assignment. To make the best global context, in this paper, we propose dense relation network (DRN) and context-restricted loss (CRL) to aggregate information. DRN uses Recurrent Neural Network (RNN) with different skip lengths spatial directions get context-aware representations while CRL helps them learn...

In the underwater domain, small object detection plays a crucial role in protection, management, and monitoring of environment marine life. Advancements deep learning have led to development many efficient techniques. However, complexity environment, limited information available from objects, constrained computational resources make challenging. To tackle these challenges, this paper presents an convolutional network model. First, CSP for lightweight (CSPSL) module is introduced enhance...

Traffic light detection technology can assist drivers in making decisions and has potential applications autonomous driving to reduce loss of life property. However, the traffic objects is challenging due light’s small size complex backgrounds, it difficult for current meet real-time high accuracy requirements. In this work, we identify that inappropriate feature integration static parameters during detector training limit model efficiency. Specifically, using same criteria all object sizes...

Modern marine ranching construction has drawn growing attention of relevant planning authorities and enterprises with the potential value oceans becoming apparent. To satisfy demand for a successful construction, site selection is considered as first fundamental procedure. This work aims to help find optimal by introducing methodological evaluation framework solving this critical problem. Firstly, advanced CRiteria Importance Through Inter-criteria Correlation (CRITIC) method extended using...

As a classic and well-performed deep convolutional neural network, DenseNet links every layer to each of its preceding layers via skip connections. However, the dense connectivity leads much redundance, consuming lots computational resources. In this paper, automatically prune redundant connections in DenseNet, we introduce novel reinforcement learning method called automatic sparsification (ADS). ADS, use adjacent matrix represent design an agent using recurrent networks (RNNs) sparsify...

Semantic image segmentation, which assigns labels in pixel level, plays a central role understanding. Recent approaches have attempted to harness the capabilities of deep learning. However, one problem these methods is that convolutional neural network gives little consideration correlation among pixels. To handle this issue, paper, we propose novel named RelationNet, utilizes CNN and RNN aggregate context information. Besides, spatial loss applied train RelationNet align features pixels...

Abstract We present a simple multi-scale learning network for image classification that is inspired by the MobileNet. The proposed method has two advantages: (1) It uses block with depthwise separable convolutions, which forms multiple sub-networks increasing width of while keeping computational resources constant. (2) combines residual connections and accelerates training networks significantly. experimental results show strong performance compared to other popular models on different datasets.

Data quantization has been proved to be an effective method compress deep neural networks (DNNs) by using less bits represent the parameters and intermediate data. The bit width of data directly affects memory footprint, computing capability, energy consumption during computation DNN models. Although there have numerous existing studies on quantization, is still no quantitative analysis methods, which results in empirical with unpredictable accuracy loss. To address this problem, we propose...

Fault-tolerance is very important in hard real-time heterogeneous systems, especially safety-critical since faults can result a disaster. Such systems require that an application operate normally even when processor subject to failures under given time constraint. In this paper, we tackle the problem of scheduling tasks on with constraint and ability fault-tolerance, while considering communication overhead. The NP-hard propose heuristic algorithm DB-FTSA solve it. based active replication...

Conventional facemask detection algorithms face challenges of insufficient accuracy, large model size, and slow computation speed, limiting their deployment in real-world scenarios, especially on edge devices. Aiming at addressing these issues, we proposed a DB-YOLO intelligent algorithm, which is lightweight solution that leverages bidirectional weighted feature fusion. Our method built the YOLOv5 algorithm model, replacing original backbone network with ShuffleNetv2 to reduce parameters...

With the rapid development of automobile industry, demand for autonomous driving becomes more and urgent, traffic sign recognition technology in is an indispensable technology. This paper proposes a GoogLeNet based convolutional neural network signs. improves each underlying Inception Modules adds Batch Normalization layer, effectively avoiding over-fitting network. We use sparse structure that conforms to Hebbain principle reduce parameters improve generalization ability network, which can...

Point triangulation probes have been in use industry for half a century. In that time, there has not any big changes the mechanism by which they operate A point laser gage bought today about same ratio of standard deviation to measurement range, or dynamic as one 30+ years ago. significant limiting factor range such sensors is noise seen sensor, consists both classic speckle noise, but also effects surface texture on reflected beam. This paper will discuss four different methods, based upon...

Traffic signs detection and segmentation is one of the important parts advanced driving assistance system. But there are predictable difficulties in detecting traffic from images or videos car cameras owing to next reasons: usually small-sized medium-sized objects, quantity imbalance between different existed public data sets. Therefore, two main developments have been proposed this paper. Firstly, an improved TT-100K-HHU sign set based on TT-100K constructed. New collected Tencent Street...

Sensory evaluation is one of the key steps in recipe product design. With development compute intelligence technology, many methods such as artificial neural network, decision tree, regression, etc are used to solve problems sensory evaluation. This becomes more and popular. But generalization ability using single model needs be improved. paper uses bagging algorithm for ensemble learning carry out compares it with classifier m5p. Through feature selection, we improve accuracy decrease...

The Dagu River aquifer in the NW part of Qingdao city is composed alluvial sediments. This an important source water indispensable for industrial, urban and agricultural development city, China. purpose this study to perform a groundwater vulnerability assessment on using Mapinfo 7.0 geographic information system DRASTIC method that serves as tool assessment, management protection quality. Based soil permeability, depth water, hydraulic conductivity, topography, subjective ratings variables...