602 research outputs found
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
Vision-based vehicle detection approaches achieve incredible success in
recent years with the development of deep convolutional neural network (CNN).
However, existing CNN based algorithms suffer from the problem that the
convolutional features are scale-sensitive in object detection task but it is
common that traffic images and videos contain vehicles with a large variance of
scales. In this paper, we delve into the source of scale sensitivity, and
reveal two key issues: 1) existing RoI pooling destroys the structure of small
scale objects, 2) the large intra-class distance for a large variance of scales
exceeds the representation capability of a single network. Based on these
findings, we present a scale-insensitive convolutional neural network (SINet)
for fast detecting vehicles with a large variance of scales. First, we present
a context-aware RoI pooling to maintain the contextual information and original
structure of small scale objects. Second, we present a multi-branch decision
network to minimize the intra-class distance of features. These lightweight
techniques bring zero extra time complexity but prominent detection accuracy
improvement. The proposed techniques can be equipped with any deep network
architectures and keep them trained end-to-end. Our SINet achieves
state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on
the KITTI benchmark and a new highway dataset, which contains a large variance
of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems
(T-ITS
GPT-Prompt Controlled Diffusion for Weakly-Supervised Semantic Segmentation
Weakly supervised semantic segmentation (WSSS), aiming to train segmentation
models solely using image-level labels, has received significant attention.
Existing approaches mainly concentrate on creating high-quality pseudo labels
by utilizing existing images and their corresponding image-level labels.
However, the quality of pseudo labels degrades significantly when the size of
available dataset is limited. Thus, in this paper, we tackle this problem from
a different view by introducing a novel approach called GPT-Prompt Controlled
Diffusion (GPCD) for data augmentation. This approach enhances the current
labeled datasets by augmenting with a variety of images, achieved through
controlled diffusion guided by GPT prompts. In this process, the existing
images and image-level labels provide the necessary control information, where
GPT is employed to enrich the prompts, leading to the generation of diverse
backgrounds. Moreover, we integrate data source information as tokens into the
Vision Transformer (ViT) framework. These tokens are specifically designed to
improve the ability of downstream WSSS framework to recognize the origins of
augmented images. Our proposed GPCD approach clearly surpasses existing
state-of-the-art methods. This effect is more obvious when the amount of
available data is small, demonstrating the effectiveness of our method
Traffic Volume Forecasting Model of Freeway Toll Stations During Holidays – An SVM Model
Support vector machine (SVM) models have good performance in predicting daily traffic volume at toll stations, however, they cannot accurately predict holiday traffic volume. Therefore, an improved SVM model is proposed in this paper. The paper takes a toll station in Heilongjiang, China as an example, and uses the daily traffic volume as the learning set. The current and previous 7-day traffic volumes are used as the dependent and independent variables for model learning, respectively. This paper found that the basic SVM model is not accurate enough to forecast the traffic volume during holidays. To improve the model accuracy, this paper first used the SVM model to forecast non-holiday traffic volumes, and proposed a prediction method using quarterly conversion coefficients combined with the SVM model to construct an improved SVM model. The result of the prediction showed that the improved SVM model in this paper was able to effectively improve accuracy, making it better than in the basic SVM and GBDT model, thus proving the feasibility of the improved SVM model
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