UnetDVH-Linear: Linear Feature Segmentation by Dilated Convolution with Vertical and Horizontal Kernels.

Linear feature extraction is crucial for special objects in semantic segmentation networks, such as slot marking and lanes. The objects with linear characteristics have global contextual information dependency. It is very difficult to capture the complete information of these objects in semantic segmentation tasks. To improve the linear feature extraction ability of the semantic segmentation network, we propose introducing the dilated convolution with vertical and horizontal kernels (DVH) into the task of feature extraction in semantic segmentation networks. Meanwhile, we figure out the outcome if we put the different vertical and horizontal kernels on different places in the semantic segmentation networks. Our networks are trained on the basis of the SS dataset, the TuSimple lane dataset and the Massachusetts Roads dataset. These datasets consist of slot marking, lanes, and road images. The research results show that our method improves the accuracy of the slot marking segmentation of the SS dataset by 2%. Compared with other state-of-the-art methods, our UnetDVH-Linear (v1) obtains better accuracy on the TuSimple Benchmark Lane Detection Challenge with a value of 97.53%. To prove the generalization of our models, road segmentation experiments were performed on aerial images. Without data argumentation, the segmentation accuracy of our model on the Massachusetts roads dataset is 95.3%. Moreover, our models perform better than other models when training with the same loss function and experimental settings. The experiment result shows that the dilated convolution with vertical and horizontal kernels will enhance the neural network on linear feature extraction.

Parking Slot Detection on Around-View Images Using DCNN.

Due to the complex visual environment and incomplete display of parking slots on around-view images, vision-based parking slot detection is a major challenge. Previous studies in this field mostly use the existing models to solve the problem, the steps of which are cumbersome. In this paper, we propose a parking slot detection method that uses directional entrance line regression and classification based on a deep convolutional neural network (DCNN) to make it robust and simple. For parking slots with different shapes and observed from different angles, we represent the parking slot as a directional entrance line. Subsequently, we design a DCNN detector to simultaneously obtain the type, position, length, and direction of the entrance line. After that, the complete parking slot can be easily inferred using the detection results and prior geometric information. To verify our method, we conduct experiments on the public ps2.0 dataset and self-annotated parking slot dataset with 2,135 images. The results show that our method not only outperforms state-of-the-art competitors with a precision rate of 99.68% and a recall rate of 99.41% on the ps2.0 dataset but also performs a satisfying generalization on the self-annotated dataset. Moreover, it achieves a real-time detection speed of 13 ms per frame on Titan Xp. By converting the parking slot into a directional entrance line, the specially designed DCNN detector can quickly and effectively detect various types of parking slots.

Adductor canal block with periarticular infiltration versus periarticular infiltration alone after total knee arthroplasty: A randomized controlled trial protocol.

BACKGROUND: Effective postoperative analgesia may enhance early rehabilitation after total knee arthroplasty (TKA). The purpose of this study is to perform a randomized controlled trial to compare the efficiency of adductor canal block (ACB) with periarticular infiltration (PAI) versus PAI alone for early postoperative pain treatment after TKA. METHODS: After institutional review board approval, written informed consent was obtained from patients undergoing elective TKA. Subjects were randomized into 2 groups as follows: adductor canal blockade with 30 mL of 0.5% ropivacaine and 100 mcg of clonidine. All patients received a periarticular infiltration mixture intraoperatively with scheduled and patient requested oral and IV analgesics postoperatively for breakthrough pain. The primary outcome was morphine consumption in the first 24 hours. Secondary outcomes included pain scores, morphine consumption at 48 hours, opioid-related side effects (post-operative nausea/vomiting, sedation scores), functional outcomes, quadriceps strength, and length of hospital stay. CONCLUSIONS: For the present trial, we hypothesized that patients receiving adductor canal block + PAI would have significantly lower morphine consumption and pain scores after surgery. TRIAL REGISTRATION NUMBER: researchregistry5490.