This research work aims to develop an image captioning system utilizing deep learning techniques. The pre-trained VGG-16 model is employed to extract image features, while an innovative encoder-decoder architecture is...
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The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver...
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In the contemporary world, humanoid robots are likely to play a key role in various fields, including health care, domestic service, hospitality, business, and military and security activities. The robots are employed...
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With the rapid development of autonomous Vehicls technology, the detection of surrounding pedestrians, vehicles, Cyclists and other targets by autonomous vehicles is an indispensable technology, especially for pedestr...
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ISBN:
(纸本)9798350363043
With the rapid development of autonomous Vehicls technology, the detection of surrounding pedestrians, vehicles, Cyclists and other targets by autonomous vehicles is an indispensable technology, especially for pedestrian detection. Therefore, there aremore and more related algorithmms and network models based on target recognition. In recent years, many scholars have stagnated in the process of discovering new algorithms and models and have rarely improved (such as SECOND, PointRCNN, PointPillars, etc.) These classic models, due to the different configuration environments of these model codes and the need to redownload each time you want to run a new model, are very time consuming and energy consuming. In order to solve these difficulties, we chose to use the OpenPCDet target detection framework to improve these models. This framework integrates all the above original object detection models to facilitate us to improve and compare the indicators between the models, and in the comparison of the results of the original model built in the OpenPCDet framework, it is found that the PointPillars modelusing 3D single-stage object detection is the most suitable for autonomous Vehicles. The recognition speed of the original PointPillars for vehicles, pedestrians and other objects can fully meet the use of autonomous Vehicles technology, but the accuracy of object recognition, especially in pedestrian detection, needs to be improved. In this regard, we propose a SelfAttention-PointPillars model. Based on the architecture of the PointPillars model and the idea of self-attention, we use our own pillar amount and modify the original backbone structure into our own self-attention network to improve the accuracy of identifying target pedestrians. We also improved the original L1 loss function into a faster weighted L2 function and we also replaced the activation function with the more efficient LeakyRelu function. Therefore, this paper mainly introduces the OpenPCDet target detect
In the task of public opinion sentiment analysis, short-text sentiment analysis and public opinion network sentiment evolution models play a crucial role. This paper provides an in-depth discussion of public opinion s...
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To address the limitations of current methods in detecting small objects, such as pedestrians and cyclists, within autonomous driving scenarios, we propose a novel 3D object detection algorithm based on an improved Pi...
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Traditional salient object detection (SOD) methods heavily rely on large-scale pixel-level datasets, making them both time-consuming and expensive. However, it is a significant challenge to effectively integrate long-...
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During vehicle operation, obstacles or other vehicles may require lane changes, necessitating the coordination of both lateral and longitudinal control. In predictable environments, rule-based lane-changing methods ar...
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This paper proposes a multi-object tracking algorithm for distorted video images captured by a fisheye camera. The method addresses the issue of residual shortening distortion in calibrated pedestrian images. It utili...
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The goal of multi-object tracking(MOT) is to detect and track interested objects in videos. It is more challenging to track multiple players in soccer videos, and most existing detection and tracking approaches are no...
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