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An autonomous vehicle must accurately detect its surrounding environment to operate reliably. Adverse weather conditions (ADWC) are snow, rain, sand, and haze, badly affect the quality of vehicle detection (VD) in an autonomous environment. Most existing techniques focused on VD under various weather effects such as signal control, travel pattern,...
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Autonomous vehicle all-weather operation poses significant challenges, encompassing modules from perception and decision-making to path planning and control. The complexity arises from the need to address adverse weather conditions like rain, snow, and fog across the autonomy stack. Conventional model-based and single-module approaches often lack h...
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... Extended author information available on the last page of the article has propelled the proliferation and substantial evolution of object detection networks. Some excellent detectors have been proposed, such as faster RCNN [1], SSD series [2][3][4][5], YOLO family [6][7][8][9], DETR series [10][11][12], etc., which have boosted the development of detection technology in various fields. ...
In the domain of object detection, the performance of a detector depend heavily on the quality of features extracted by the backbone network. Extraordinary feature representation has significantly improved the performance of the detector. As we all know, proposing a novel backbone structure requires meticulous structural design, a wealth of expert experience and tricks, and consumes a lot of computing resources. Therefore, it is meaningful to effectively leveraging the existing pre-trained backbones and maximize their performance to improve the accuracy of object detection. In this paper, we propose a novel Feature-enhanced Composite Backbone Network to improve the feature representation capability of the backbone, which called FECNet, equipped with Proportional Feature Fusion Module(PFF) and Multi-Granularity Information Aggregation and Interaction Method(MIAM). In particular, FECNet combines the existing backbones, which are connected by PFF, and FECNet pays more attention to extracting the discriminative features related to the object that are suitable for classification and the edge information suitable for bounding box regression through MIAM. Experiments show that FECNet is easily integrated into mainstream detectors and improve their performances. On the COCO 2017 dataset, employing ResNet50 as the foundational backbone, our approach attains a notable 3 percent increment in performance within the Fast R-CNN framework. Simultaneously, our method yields 2.1 percent enhancement when applied to the ResNet101 + Cascade R-CNN. And it’s worth noting that applying our approach on the backbone swin transformer which is based on the trasformer structure gets an increase of more than 2 percent.
