Han Li

Industrial nameplates serve as a means of conveying critical information and parameters. In this work, we propose a novel approach for rectifying industrial nameplate pictures utilizing a Probabilistic Hough Transform. Our method effectively corrects for distortions and clipping, and features a collection of challenging nameplate pictures for analysis. To determine the corners of the nameplate, we employ a progressive Probability Hough Transform, which not only enhances detection accuracy but also possesses the ability to handle complex industrial scenarios. The results of our approach are clear and readable nameplate text, as demonstrated through experiments that show improved accuracy in model identification compared to other methods.

In the field of computer vision, training a well-performing model on a dataset with a long-tail distribution is a challenging task. To address this challenge, image resampling is usually introduced as a simple and effective solution. However, when performing instance segmentation tasks, there may be multiple classes in one image. Hence, image resampling alone is not enough to obtain a sufficiently balanced distribution at the level of target data volume. In this paper, we propose an improved instance segmentation method for long-tail datasets based on Mask R-CNN. Specifically, an object-centric memory bank is used to establish an object-centric storage strategy that can solve the imbalance problem with respect to categories. In the testing phase, a post-processing calibration is used to adjust each class logit to change the confidence score, which improves the prediction score of tail classes. A discrete cosine transform-based mask is used to obtain high-quality masks, which improves segmentation accuracy. The evaluation of the proposed method on the LVIS dataset demonstrates its effectiveness. The proposed method improves the AP performance of EQL by 2.2%.

The anchor-free-based object detection method is a crucial part in an autonomous driving system because of its low computing cost. However, the under-fitting of positive samples and over-fitting of negative samples affect the detection performance. An aspect-aware anchor-free detector is proposed in this paper to address this problem. Specifically, it adds an aspect prediction head at the end of the detector, which can learn different distributions of aspect ratios between other objects. The sample definition method is improved to alleviate the problem of positive and negative sample imbalance. A loss function is designed to strengthen the learning weight of the center point of the network. The validation results show that the AP50 and AP75 of the proposed method are 97.3% and 93.4% on BCTSDB, and the average accuracies of the car, pedestrian, and cyclist are 92.7%, 77.4%, and 78.2% on KITTI, respectively. The comparison results demonstrate that the proposed algorithm is better than existing anchor-free methods.