Intelligent Intersection Vehicle and Pedestrian Detection Based on Convolutional Neural Network

Abstract

The preprocessed images are input to a pretrained neural network to obtain the corresponding feature mapping, and the corresponding region of interest is set for each point in the feature mapping to obtain multiple candidate feature regions; subsequently, these candidate feature regions are fed into a region proposal network and a deep residual network for binary classification and BB regression, and some of the candidate feature regions are filtered out, and the remaining feature regions are subjected to ROIAIign operation; finally, classification, BB regression, and mask generation are performed on these feature regions, and full convolutional nerve network operation is performed in each feature region and output. To further identify the specific model of the vehicle, this paper proposes a multifeature model recognition method that fuses the improved model with the optimized Mask R-CNN algorithm. A vehicle local feature dataset including vehicle badges, lights, air intake grille, and whole vehicle outline is established to simplify the network structure of model. Meanwhile, its detection frame generation process and the adjustment rules of overlapping frame confidence in nonmaximum suppression are improved for coarse vehicle localization. Then, the generated vehicle detection frames after localization are output to the Mask R-CNN algorithm after further optimizing the RPN structure. The localized vehicle detection frames are then output to the Mask R-CNN algorithm after further optimization of the RPN structure for local feature recognition, and good recognition results are achieved. Finally, this paper establishes a distributed server-based vehicle recognition system, which mainly includes database module, file module, feature extraction and matching module, message queue module, WEB module, and vehicle detection module. Due to the limitations of traditional region generation methods, this paper provides a brief analysis of the region generation network in the Faster R-CNN algorithm and details the loss calculation principle of the output layer.

Optimization study of convolutional neural network model based on computer simulation context

“…The stream interface defines the handshake behaviour of data interaction between modules. It enables data to be sent and received correctly [8]. The interface timing representation is shown in Figures 3 and 4 This data handshake is a synchronous transmission method with three key signals: the data validity signal on the sender side, the ready signal on the receiver side, and the payload signal on the sender side [9].…”

Vehicle detection algorithm based on lightweight YOLOX

“…The authors analyze the labels of the dataset to set a priori box that is more in line with pedestrians and vehicles and combine multi-scale training to improve the detection accuracy. Yang et al [12] improved Mask R-CNN to detect pedestrians and vehicles, and built a real-time vehicle identi cation system. Wang et al [13] proposed a soft-weighted method that fused RetinaNet [14] and Cascade-RCNN [15], and the results proved that this ensemble model has an excellent detection ability for overlapping objects.…”