Meanwhile, the ACmix module at the end of the model backbone network solves the category imbalance problem by adaptively adjusting the contrast and sample mixing, thus enhancing the detection accuracy in complex scenes. In the experiments on the PDT [5/8 of https://arxiv.org/abs/2504.11165v1]
April 16, 2025 at 6:09 AM
Meanwhile, the ACmix module at the end of the model backbone network solves the category imbalance problem by adaptively adjusting the contrast and sample mixing, thus enhancing the detection accuracy in complex scenes. In the experiments on the PDT [5/8 of https://arxiv.org/abs/2504.11165v1]
improved YOLOv7x-based anomaly detection algorithm for power equipment. First, the ACmix convolutional mixed attention mechanism module is introduced to effectively suppress background noise and irrelevant features, thereby [2/5 of https://arxiv.org/abs/2502.17961v1]
February 26, 2025 at 5:58 AM
improved YOLOv7x-based anomaly detection algorithm for power equipment. First, the ACmix convolutional mixed attention mechanism module is introduced to effectively suppress background noise and irrelevant features, thereby [2/5 of https://arxiv.org/abs/2502.17961v1]
proposed with ACmix module and the small object detection head. Then, the BoT-SORT algorithm is utilized for pothole tracking, while DepthAnything V2 generates depth maps for each frame. With the obtained depth maps and potholes labels, a novel [5/7 of https://arxiv.org/abs/2505.21049v1]
May 28, 2025 at 6:10 AM
proposed with ACmix module and the small object detection head. Then, the BoT-SORT algorithm is utilized for pothole tracking, while DepthAnything V2 generates depth maps for each frame. With the obtained depth maps and potholes labels, a novel [5/7 of https://arxiv.org/abs/2505.21049v1]