Optimized Convolutional Neural Network for Road Detection with Structured Contour and Spatial Information for Intelligent Vehicle System

Abstract

“Road detection is said to be a major research area in remote sensing analysis and it is usually complex due to the data complexities as it gets varied in appearance with minor inter-class and huge intra-class variations that often cause errors and gaps in the extraction of the road”. Moreover, the majority of supervised learning techniques endure from the high price of manual annotation or inadequate training data. Thereby, this paper intends to introduce a new model for road detection. This work exploits a siamesed fully convolutional network (named as “s-FCN-loc”) based on VGG-net architecture that considers semantic contour, RGB channel and location prior for segmenting road regions precisely. As a major contribution, super pixel segmentation was carried out, where the RGB images are given as input to the FCN network and the road regions of images are set as a target. Further, the segmented outputs are fused using AND operation to attain the final segmented output that detects the road regions accurately. To make the detection more accurate, the convolutional layers of FCN are optimally chosen by a new improved model termed as distance oriented sea lion algorithm (DSLnO) model. The presented DSLnO + FCN model has achieved a minimal value of negative measures and accuracy is 8.2% higher than traditional methods. Finally, the presented method is evaluated on the KITTI road detection dataset, and achieves a better result. The analysis was done with respect to positive measures and negative measures.

Enhancing hazardous material vehicle detection with advanced feature enhancement modules using HMV-YOLO

“…Compared to other improved CNN methods of the same type, this framework effectively enhances computational efficiency. Furthermore, Dewangan and Sahu ( 2022 ) proposed a road detection model based on Siamese Fully Convolutional Network (s-FCN-loc). This model combines semantic contours, RGB channels, and prior location information, achieving precise segmentation of road areas.…”

Efficient Multi-Source Self-Attention Data Fusion for FDIA Detection in Smart Grid

“…The whole hybrid deep-network model is shown in Figure 2. Firstly, the model symmetrically divides the processed sensor data into two copies; one is used for extracting convolutional features [33,34] and the other is for extracting temporal features. The CNN layer that is used to extract the convolutional features mainly consists of three one-dimensional convolutional operations (Conv1D) containing 5 × 5, 3 × 3, and 3 × 3 convolutional kernels, respectively, and all the convolutional layers have a convolutional step size of 1.…”

Path Following of an Underwater Snake-like Robot Exposed to Ocean Currents and Locomotion Efficiency Optimization Based on Multi-Strategy Improved Sparrow Search Algorithm

“…The differential evolution algorithm is a heuristic algorithm proposed by Storm et al [42]. The differential variation is selected to perturb individuals with better than average fitness, and its mutation strategy is shown in Equation (31).…”

“…The development of bionic intelligent algorithms has provided new solutions for optimization problems, which can achieve real-time computation due to their fast iteration speed. D. K. Dewangan has optimized the neural network using a bionic intelligence algorithm, which has greatly improved the accuracy of the output of the neural network [31,32]. An improved pigeon-inspired algorithm has been proposed to optimize robot system parameters in real time, aiming to overcome the challenges of slow convergence speed and susceptibility to local optimum [33].…”