In order to develop an underwater sea cucumber collecting robot, it is necessary to use the machine vision method to realize sea cucumber recognition and location. An identification and location method of underwater sea cucumber based on improved You Only Look Once version 5 (YOLOv5) is proposed. Due to the low contrast between sea cucumbers and the underwater environment, the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm was introduced to process the images to enhance the contrast. In order to improve the recognition precision and efficiency, the Convolutional Block Attention Module (CBAM) is added. In order to make small target recognition more precise, the Detect layer was added to the Head network of YOLOv5s. The improved YOLOv5s model and YOLOv5s, YOLOv4, and Faster-RCNN identified the same image set; the experimental results show improved YOLOv5 recognition precision level and confidence level, especially for small target recognition, which is excellent and better than other models. Compared to the other three models, the improved YOLOv5s has higher precision and detection time. Compared with the YOLOv5s, the precision and recall rate of the improved YOLOv5s model are improved by 9% and 11.5%, respectively.

The proposed method for tire specification character recognition based on the YOLOv5 network aimed to address the low efficiency and accuracy of the current character recognition methods. The approach involved making three major modifications to the YOLOv5 network to improve its generalization ability, computation speed, and optimization. The first modification involved changing the coupled head in YOLOv5 to a decoupled head, which could improve the network’s generalization ability. The second modification proposed incorporating the C3-Faster module, which would replace some of the C3 modules in YOLOv5’s backbone and head and improve the network’s computation speed. Finally, the third modification proposed replacing YOLOv5’s CIoU loss function with the WIoU loss function to optimize the network. Comparative experiments were conducted to validate the effectiveness of the proposed modifications. The C3-Faster module and the WIoU loss function were found to be effective, reducing the training time of the improved network and increasing the mAP by 3.7 percentage points in the ablation experiment. The experimental results demonstrated the effectiveness of the proposed method in improving the accuracy of tire specification character recognition and meeting practical application requirements. Overall, the proposed method showed promising results for improving the efficiency and accuracy of automotive tire specification character recognition, which has potential applications in various industries, including automotive manufacturing and tire production.

The Efficient Convolution Operators for Tracking (ECO) algorithm has garnered considerable attention in both academic research and practical applications due to its remarkable tracking efficacy, yielding exceptional accuracy and success rates in various challenging contexts. However, the ECO algorithm heavily relies on the deep learning Visual Geometry Group (VGG) network model, which entails complexity and substantial computational resources. Moreover, its performance tends to deteriorate in scenarios involving target occlusion, background clutter, and similar challenges. To tackle these issues, this study introduces a novel enhancement to the pedestrian tracking algorithm. Specifically, the VGG network is substituted with a lightweight MobileNet v2 model, thereby reducing computational demands. Additionally, a Double Attention Networks (A2-Net) module is incorporated to augment the extraction of crucial information, while pre-training techniques are integrated to expedite model convergence. Experimental results demonstrate that the C-ECO algorithm achieves comparable accuracy and success rates to the conventional ECO algorithm, despite reducing the model size by 27.96% and increasing the tracking frame rate by 46.11%. Notably, when compared to other prevalent tracking algorithms, the C-ECO algorithm exhibits an accuracy of 82.20% and a success rate of 64.72%. These findings underscore the enhanced adaptability of the C-ECO algorithm in complex environments, offering a more lightweight model while delivering superior tracking capabilities.

This paper proposes a novel fingerprint matching algorithm. The matching process was divided into three stages. In first stage, local orientation structures matching was performed to evaluate the similarity of orientation information around the minutiae. In the second stage, local minutia structure matching was performed to evaluate the similarity of neighborhood minutiae distribution around the minutiae. In the third stage, the matching pair sets was built by matching the global minutia structures that extended from the local minutia structure, which evaluated the global similarity. The final similarity between two fingerprints was gotten according to the matching scores calculated from three stages. Experiments on four databases of FVC2002 show that this fingerprints matching technique is really effective.

With the rapid advancement of industrial automation, surface defect detection technology for metal shafts has become crucial for improving product quality, reducing production costs, and optimizing manufacturing efficiency. Traditional methods for surface defect detection, often reliant on manual inspection, suffer from low efficiency and poor accuracy, making them inadequate for modern industrial demands. To address these challenges, this paper introduces WRI-YOLO11, an improved YOLO11n-based network model designed for detecting surface defects in metal shafts. First, a novel C3k2-WTConv module, based on wavelet transform, is proposed to enhance the network’s feature extraction capability while reducing model complexity and computational cost. Second, an RC-FPN module is developed to improve the model’s ability to fuse multi-scale features. Finally, the Inner-PIoUv2 loss function is introduced, enabling more precise target boundary localization to enhance segmentation and positioning accuracy. Compared to the original network model, the proposed algorithm improves mAP, Precision, and Recall by 4.4%, 4.6%, and 1.2%, respectively. Additionally, it reduces the number of parameters by 0.02 M, with a slight increase in FLOPs by 0.4G. These results demonstrate the superior performance of WRI-YOLO11 in detecting surface defects in metal shafts.