Abstract—Bounding box regression plays a crucial role in the field of object detection, and the positioning accuracy of object detection largely depends on the loss function of bounding box regression. Existing researchs improve regression performance by utilizing the geometric relationship between boundin...
Search before asking I have searched the YOLOv5 issues and discussions and found no similar questions. Question Hi, In train.py, computeLoss function utilizes bbox_iou function in utils/metrics.py. There are different types such as IoU, ...
@wanterlimloss.py is not a runnable file, it has functions used for loss computation. The function that computes CIoU between pairs of boxes is here: yolov5/utils/general.py Lines 188 to 231 in2026d4c defbbox_iou(box1,box2,x1y1x2y2=True,GIoU=False,DIoU=False,CIoU=False,eps=1e-9...
The model integrates the Squeeze-and-Excitation attention mechanism, the deformable convolution C2f module, and the smooth IoU loss function, achieving significant improvements in detection accuracy and robustness in various complex environments. Experimental results show that the enhanced YOLOv8 model ...
YOLOv8; SE attention mechanism; deformable convolution C2f module; smooth IoU loss function; multi-target detection; complex traffic environment; object detection; intelligent transportation1. Introduction In transportation systems, accurate detection of road surface information is essential to ensure road ...
In response, we propose a Powerful-IoU (PIoU) loss function, which combines a target size-adaptive penalty factor and a gradient-adjusting function based on anchor box quality. The PIoU loss guides anchor boxes to regress along efficient paths, resulting in faster convergence than existing IoU-...
In addition, we propose a more powerful Corner Distance IoU (intersection over union) loss function so that the algorithm can better regression to the bounding box. In the experiments, the tracker was extensively evaluated on the object tracking benchmark data sets, OTB2013 and OTB2015, and ...
Finally, to speed up the convergence of the loss function, the SIoU loss function is introduced to replace Complete-IoU (CIoU) in the original algorithm. In order to verify the authenticity of the improved algorithm, we conduct a series of experiments on the ...
Torchvision NMS is a function in Torchvision>=0.3, and our Cluster-NMS can be applied to any projects that use low version of Torchvision and other deep learning frameworks as long as it can do matrix operations.No other import, no need to compile, less iteration, fully GPU-accelerated and...
While trackers adopt a maximum overlap method based on an intersection-over-union (IoU) loss to mitigate this problem, there are defects in the IoU loss itself, that make it impossible to continue to optimize the objective function when a given bounding box is completely contained within/without...