Additionally, we introduce a 5-layer feature pyramid network (FPN) structure and a multi-scale spatial attention mechanism to improve feature saliency for objects of different scales, thereby enhancing the detection accuracy of the network. Experimental results demonstrate that our YOLOv...
YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspectivedoi:10.3390/app132011402object detectionmonocular 3D object detectionroadside perspectiveCurrent autonomous driving systems predominantly focus on 3D object perception from the vehicle's perspective. However, the single-...
For a 3D point 𝑋=[𝑥,𝑦,𝑧]𝑇X=[x,y,z]T, it can be projected onto the image plane as 𝑌=[𝑢,𝑣,1]𝑇Y=[u,v,1]T using the camera projection matrix: 3.2. Model Structure In terms of the model structure, YOLOv7-3D can be divided into three parts: the backbone...
Through validation using CT slice images of the 3D printed lattice structure, the results indicate the recognition accuracy of 96.2%, surpassing the conventional YOLOv7 approach by 1.7%. The effectiveness and superiority of the methods suggested in this study are supported by these findings.Yintang...
For a 3D point 𝑋=[𝑥,𝑦,𝑧]𝑇X=[x,y,z]T, it can be projected onto the image plane as 𝑌=[𝑢,𝑣,1]𝑇Y=[u,v,1]T using the camera projection matrix: 3.2. Model Structure In terms of the model structure, YOLOv7-3D can be divided into three parts: the backbone...
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