Journal of Real-Time Image Processing最新文献

To reduce the hardware implementation resource consumption of the two-dimensional transform component in H.266 VVC, a unified hardware structure is proposed that supports full-size Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and full-size Low-Frequency Non-Separable Transform (LFNST). This paper presents an area-efficient hardware architecture for two-dimensional transforms based on a general Regular Multiplier (RM) and a high-throughput hardware design for LFNST in the context of H.266/VVC. The first approach utilizes the high-frequency zeroing characteristics of VVC and the symmetric properties of the DCT-II matrix, allowing the RM-based architecture to use only 256 general multipliers in a fully pipelined structure with a parallelism of 16. The second approach optimizes the transpose operation of the input matrix for LFNST in a parallelism of 16 architecture, aiming to save storage and logic resources.

A novel real-time infrared pedestrian detection algorithm is introduced in this study. The proposed approach leverages re-parameterized convolution and channel-spatial location fusion attention to tackle the difficulties presented by low-resolution, partial occlusion, and environmental interference in infrared pedestrian images. These factors have historically hindered the accurate detection of pedestrians using traditional algorithms. First, to tackle the problem of weak feature representation of infrared pedestrian targets caused by low resolution and partial occlusion, a new attention module that integrates channel and spatial is devised and introduced to CSPDarkNet53 to design a new backbone CSLF-DarkNet53. The designed attention model can enhance the feature expression ability of pedestrian targets and make pedestrian targets more prominent in complex backgrounds. Second, to enhance the efficiency of detection and accelerate convergence, a multi-branch decoupled detector head is designed to operate the classification and location of infrared pedestrians separately. Finally, to improve poor real-time without losing precision, we introduce the re-parameterized convolution (Repconv) using parameter identity transformation to decouple the training process and detection process. During the training procedure, to enhance the fitting ability of small convolution kernels, a multi-branch structure with convolution kernels of different scales is designed. Compared with the nice classical detection algorithms, the results of the experiment show that the proposed RCSLFNet not only detects partial occlusion infrared pedestrians in complex environments accurately but also has better real-time performance on the KAIST dataset. The mAP@0.5 reaches 86% and the detection time is 0.0081 s, 2.9% higher than the baseline.

The current apple detection algorithms fail to accurately differentiate obscured apples from pickable ones, thus leading to low accuracy in apple harvesting and a high rate of instances where apples are either mispicked or missed altogether. To address the issues associated with the existing algorithms, this study proposes an improved YOLOv5s-based method, named YOLOv5s-BC, for real-time apple detection, in which a series of modifications have been introduced. First, a coordinate attention block has been incorporated into the backbone module to construct a new backbone network. Second, the original concatenation operation has been replaced with a bi-directional feature pyramid network in the neck network. Finally, a new detection head has been added to the head module, enabling the detection of smaller and more distant targets within the field of view of the robot. The proposed YOLOv5s-BC model was compared to several target detection algorithms, including YOLOv5s, YOLOv4, YOLOv3, SSD, Faster R-CNN (ResNet50), and Faster R-CNN (VGG), with significant improvements of 4.6%, 3.6%, 20.48%, 23.22%, 15.27%, and 15.59% in mAP, respectively. The detection accuracy of the proposed model is also greatly enhanced over the original YOLOv5s model. The model boasts an average detection speed of 0.018 s per image, and the weight size is only 16.7 Mb with 4.7 Mb smaller than that of YOLOv8s, meeting the real-time requirements for the picking robot. Furthermore, according to the heat map, our proposed model can focus more on and learn the high-level features of the target apples, and recognize the smaller target apples better than the original YOLOv5s model. Then, in other apple orchard tests, the model can detect the pickable apples in real time and correctly, illustrating a decent generalization ability. It is noted that our model can provide technical support for the apple harvesting robot in terms of real-time target detection and harvesting sequence planning.

Due to the increasing demand for artificial intelligence technology in today’s society, the entire industrial production system is undergoing a transformative process related to automation, reliability, and robustness, seeking higher productivity and product competitiveness. Additionally, many hardware platforms are unable to deploy complex algorithms due to limited resources. To address these challenges, this paper proposes a computationally efficient lightweight convolutional neural network called Brightness Improved by Light-DehazeNet, which removes the impact of fog and haze to reconstruct clear images. Additionally, we introduce an efficient hardware accelerator architecture based on this network for deployment on low-resource platforms. Furthermore, we present a brightness visibility restoration method to prevent brightness loss in dehazed images. To evaluate the performance of our method, extensive experiments were conducted, comparing it with various traditional and deep learning-based methods, including images with artificial synthesis and natural blur. The experimental results demonstrate that our proposed method excels in dehazing ability, outperforming other methods in comprehensive comparisons. Moreover, it achieves rapid processing speeds, with a maximum frame rate of 105 frames per second, meeting the requirements of real-time processing.

Transmission line insulators often operate in challenging weather conditions, particularly on rainy days. Continuous exposure to humidity and rain accelerates the aging process of insulators, leading to a decline in insulating material performance, the occurrence of cracks, and deformation. This situation poses a significant risk to the operation of the power system. Scene images collected on rainy days are frequently obstructed by rain lines, resulting in blurred backgrounds that significantly impact the performance of detection models. To improve the accuracy of insulator defect detection in rainy day environments, this paper proposes the DRI-Net (Derain-Insulator-net) detection model. Firstly, a dataset of insulator defects in rainy weather environments is constructed. Second, designing the de-raining model DRGAN and integrating it as an end-to-end DRGAN de-raining structural layer into the input end of the DRI-Net detection model, we significantly enhance the clarity and quality of images affected by rain, thereby reducing adverse effects such as image blurring and occlusion caused by rainwater. Finally, to enhance the lightweight performance of the model, partial convolution (PConv) and the lightweight upsampling operator CARAFE are utilized in the detection network to reduce the computational complexity of the model. The Wise-IoU bounding box regression loss function is applied to achieve faster convergence and improved detector accuracy. Experimental results demonstrate the effectiveness of the DRI-Net model in the task of rainy-day insulator defect detection, achieving an average precision MAP value of 82.65% in the established dataset. Additionally, an online detection system for rainy day insulator defects is designed in conjunction with the detection model, demonstrating practical engineering applications value.

Recently, deep learning methodologies have achieved significant advancements in mineral automatic sorting and anomaly detection. However, the limited features of minerals transported in the form of small particles pose significant challenges to accurate detection. To address this challenge, we propose a enhanced mineral particle detection algorithm based on the YOLOv8s model. Initially, a C2f-SRU block is introduced to enable the feature extraction network to more effectively process spatial redundant information. Additionally, we designed the GFF module with the aim of enhancing information propagation between non-adjacent scale features, thereby enabling deep networks to more fully leverage spatial positional information from shallower networks. Finally, we adopted the Wise-IoU loss function to optimize the detection performance of the model. We also re-designed the position of the prediction heads to achieve precise detection of small-scale targets. The experimental results substantiate the effectiveness of the algorithm, with YOLO-Global achieving a mAP@.5 of 95.8%. In comparison to the original YOLOv8s, the improved model exhibits a 2.5% increase in mAP, achieving a model inference speed of 81 fps, meeting the requirements for real-time processing and accuracy.

Over the decades, implementing information technology (IT) has become increasingly common, equating to an increasing amount of data that needs to be stored, creating a massive challenge in data storage. Using a large storage capacity can solve the problem of the file size. However, this method is costly in terms of both capacity and bandwidth. One possible method is data compression, which significantly reduces the file size. With the development of IT and increasing computing capacity, data compression is becoming more and more widespread in many fields, such as broadcast television, aircraft, computer transmission, and medical imaging. In this work, we introduce an image compression algorithm based on the Huffman coding algorithm and use linear techniques to increase image compression efficiency. Besides, we replace 8-bit pixel-by-pixel compression by dividing one pixel into two 4-bit halves to save hardware capacity (because only 4-bit for each input) and optimize run time (because the number of different inputs is less). The goal is to reduce the image’s complexity, increase the data’s repetition rate, reduce the compression time, and increase the image compression efficiency. A hardware accelerator is designed and implemented on the Virtex-7 VC707 FPGA to make it work in real-time. The achieved average compression ratio is 3,467. Hardware design achieves a maximum frequency of 125 MHz.

Secret image sharing (SIS) conveys a secret image to mutually suspicious receivers by sending meaningless shares to the participants, and all shares must be present to recover the secret. This paper proposes and compares three systems for secret sharing, where a visual cryptography system is designed with a fast recovery scheme as the backbone for all systems. Then, an SIS system is introduced for sharing any type of image, where it improves security using the Lorenz chaotic system as the source of randomness and the generalized Arnold transform as a permutation module. The second SIS system further enhances security and robustness by utilizing SHA-256 and RSA cryptosystem. The presented architectures are implemented on a field programmable gate array (FPGA) to enhance computational efficiency and facilitate real-time processing. Detailed experimental results and comparisons between the software and hardware realizations are presented. Security analysis and comparisons with related literature are also introduced with good results, including statistical tests, differential attack measures, robustness tests against noise and crop attacks, key sensitivity tests, and performance analysis.

This paper deals with a challenging autonomous parking problem in which the parking slots are with various different angles. We transform the problem of parking slot detection into center keypoint detection, representing the parking slot as a T-shape to make it robust and simple. For diverse types of parking slots, we propose a T-shape parking slot detection method, called T-PSD, to extract the T-shape center information based on a self-calibrated convolution network (SCCN). This method can concurrently obtain the entrance center confidence, the relative offsets of the paired junctions, the direction of the middle line, the occupancy and the inferred type in the parking slots. Final detection results are produced by utilizing Half-Heatmap, MultiBins and Midline-Grid to more accurately extract the center keypoint, direction and occupancy, respectively. To verify the performance of our method, we conduct experiments on the public PS2.0 dataset. The results have shown that our method outperforms state-of-the-art competitors by showing recall rate of 99.86% and precision rate of 99.82%. It is capable of achieving 65 frames per second (FPS) and satisfying a real-time detection performance. In contrast to the simultaneous detection of global and local information, our SCCN detector exclusively concentrates on the T-shape center information, which achieves comparable performance and significantly accelerates the inference time without non-maximum suppression (NMS).

Deep learning-based object detection methods often grapple with excessive model parameters, high complexity, and subpar real-time performance. In response, the YOLO series, particularly the YOLOv5s to YOLOv8s methods, has been developed by scholars to strike a balance between real-time processing and accuracy. Nevertheless, YOLOv8’s precision can fall short in certain specific applications. To address this, we introduce a real-time object detection method called (eta)-RepYOLO, which is built upon the (eta)-RepConv structure. This method is designed to maintain consistent detection speeds while improving accuracy. We begin by crafting a backbone network named (eta)-EfficientRep, which utilizes a strategically designed network unit-(eta)-RepConv and (eta)-RepC2f module, to reparameterize and subsequently generate an efficient inference model. This model achieves superior performance by extracting detailed feature maps from images. Subsequently, we propose the enhanced (eta)-RepPANet and (eta)-RepAFPN as the model’s detection neck, with the addition of the (eta)-RepC2f for optimized feature fusion, thus boosting the neck’s functionality. Our innovation continues with the development of an advanced decoupled head for detection, where the (eta)-RepConv takes the place of the traditional (3 times 3) conv, resulting in a marked increase in detection precision during the inference stage. Our proposed (eta)-RepYOLO method, when applied to distinct neck modules, (eta)-RepPANet and (eta)-RepAFPN, achieves mAP of 84.77%/85.65% on the PASCAL VOC07+12 dataset and AP of 45.3%/45.8% on the MSCOCO dataset, respectively. These figures represent a significant advancement over the YOLOv8s method. Additionally, the model parameters for (eta)-RepYOLO are reduced to 10.8M/8.8M, which is 3.6%/21.4% less than that of YOLOv8, culminating in a more streamlined detection model. The detection speeds clocked on an RTX3060 are 116 FPS/81 FPS, showcasing a substantial enhancement in comparison to YOLOv8s. In summary, our approach delivers competitive performance and presents a more lightweight alternative to the SOTA YOLO models, making it a robust choice for real-time object detection applications.