Precision Of Object Detection Research Articles

FSPI-R&D: Joint Reconstruction and Detection to Enhance the Object Detection Precision of Fourier Single-Pixel Imaging

FSPI-R&D: Joint Reconstruction and Detection to Enhance the Object Detection Precision of Fourier Single-Pixel Imaging

An Improved YOLOv8 Network for Multi-Object Detection with Large-Scale Differences in Remote Sensing Images

Aiming to address the challenges of low object detection precision in remote sensing images due to high background complexity and significant target scale variations, a novel model for large-scale disparate object detection in remote sensing images is proposed based on the modified YOLOv8. The model incorporates a Context Aggregation Module (CAM) with an attention mechanism in the backbone network to exploit contextual information, enabling multi-scale feature fusion for effective small object detection. The Neck network utilizes GSConv modules and employs the Slim-Neck design paradigm to enhance model robustness, making it better suited for detecting objects in high-complexity backgrounds. Furthermore, the model adopts the Wise-IoU as the loss function, incorporating a dynamic nonmonotonic focusing mechanism and a gradient gain allocation strategy to enhance the overall performance of disparate object detection. The experimental results indicate that promising performance improvements in the face of large-scale variations in remote sensing image targets. Specifically, the model achieves mAP values of 71.4% and 90.3% on the DOTA and NWPU VHR-10 datasets, respectively, representing increases of 4.4% and 3.7% compared to the original model. Compared to other typical algorithms, it also has considerable advantages in both comprehensive detection accuracy and detection speed.

Smart Caption: Intelligent Image Description Using Transformer Decoder

The need for automated image description systems has grown significantly with the rise of multimedia content across diverse domains such as social media, digital libraries, and accessibility technologies. Smart Caption presents a novel approach for generating intelligent and context-aware image descriptions by utilizing a combination of Vision Transformers (ViTs) and Natural Language Processing (NLP) Transformer decoders. Unlike traditional convolution-based methods, Vision Transformers treat images as sequences of patches, enabling them to capture global image features more effectively. These extracted visual features are then processed by a Transformer-based decoder to produce coherent and contextually appropriate captions, bridging the gap between image recognition and natural language generation. Our approach focuses on leveraging the attention mechanisms inherent in both Vision and NLP Transformers to enhance the quality of image descriptions. The ViT architecture is designed to focus on relevant regions within an image, while the NLP Transformer decoder is adept at generating fluent and detailed descriptions by attending to the most significant visual features. This two-stage process improves the precision of object detection, relationship understanding, and scene context in generated captions. Experimental results demonstrate that Smart Caption out performs traditional methods in terms of accuracy, contextual relevance, and fluency, marking a significant step forward in the field of automated image captioning. Through this research, we aim to provide a scalable, efficient, and intelligent solution for image description, with potential applications in areas such as accessibility tools for visually impaired users, digital content indexing, and automated content generation. Our findings highlight the strengths of transformer-based models in bridging vision and language tasks, offering promising directions for future research in multimodal AI..

ACFNet: An adaptive cross-fusion network for infrared and visible image fusion

Considering the prospects for image fusion, it is necessary to guide the fusion to adapt to downstream vision tasks. In this paper, we propose an Adaptive Cross-Fusion Network (ACFNet) that utilizes an adaptive approach to fuse infrared and visible images, addressing cross-modal differences to enhance object detection performance. In ACFNet, a hierarchical cross-fusion module is designed to enrich the features at each level of the reconstructed images. In addition, a special adaptive gating selection module is proposed to realize feature fusion in an adaptive manner so as to obtain fused images without the interference of manual design. Extensive qualitative and quantitative experiments have demonstrated that ACFNet is superior to current state-of-the-art fusion methods and achieves excellent results in preserving target information and texture details. The fusion framework, when combined with the object detection framework, has the potential to significantly improve the precision of object detection in low-light conditions.

Impact of LiDAR point cloud compression on 3D object detection evaluated on the KITTI dataset

The rapid growth on the amount of generated 3D data, particularly in the form of Light Detection And Ranging (LiDAR) point clouds (PCs), poses very significant challenges in terms of data storage, transmission, and processing. Point cloud (PC) representation of 3D visual information has shown to be a very flexible format with many applications ranging from multimedia immersive communication to machine vision tasks in the robotics and autonomous driving domains. In this paper, we investigate the performance of four reference 3D object detection techniques, when the input PCs are compressed with varying levels of degradation. Compression is performed using two MPEG standard coders based on 2D projections and octree decomposition, as well as two coding methods based on Deep Learning (DL). For the DL coding methods, we used a Joint Photographic Experts Group (JPEG) reference PC coder, that we adapted to accept LiDAR PCs in both Cartesian and cylindrical coordinate systems. The detection performance of the four reference 3D object detection methods was evaluated using both pre-trained models and models specifically trained using degraded PCs reconstructed from compressed representations. It is shown that LiDAR PCs can be compressed down to 6 bits per point with no significant degradation on the object detection precision. Furthermore, employing specifically trained detection models improves the detection capabilities even at compression rates as low as 2 bits per point. These results show that LiDAR PCs can be coded to enable efficient storage and transmission, without significant object detection performance loss.

Real-Time Detection Technology of Corn Kernel Breakage and Mildew Based on Improved YOLOv5s

In order to solve low recognition of corn kernel breakage degree and corn kernel mildew degree during corn kernel harvesting, this paper proposes a real-time detection method for corn kernel breakage and mildew based on improved YOlOv5s, which is referred to as the CST-YOLOv5s model algorithm in this paper. The method continuously obtains images through the discrete uniform sampling device of corn kernels and generates whole corn kernels, breakage corn kernels, and mildew corn kernel dataset samples. We aimed at the problems of high similarity of some corn kernel features in the acquired images and the low precision of corn kernel breakage and mildew recognition. Firstly, the CBAM attention mechanism is added to the backbone network of YOLOv5s to finely allocate and process the feature information, highlighting the features of corn breakage and mildew. Secondly, the pyramid pooling structure SPPCPSC, which integrates cross-stage local networks, is adopted to replace the SPPF in YOLOv5s. SPP and CPSC technologies are used to extract and fuse features of different scales, improving the precision of object detection. Finally, the original prediction head is converted into a transformer prediction head to explore the prediction potential with a multi-head attention mechanism. The experimental results show that the CST-YOLOv5s model has a significant improvement in the detection of corn kernel breakage and mildew. Compared with the original YOLOv5s model, the average precision (AP) of corn kernel breakage and mildew recognition increased by 5.2% and 7.1%, respectively, and the mean average precision (mAP) of all kinds of corn kernel recognition is 96.1%, and the frame rate is 36.7 FPS. Compared with YOLOv4-tiny, YOLOv6n, YOLOv7, YOLOv8s, and YOLOv9-E detection model algorithms, the CST-YOLOv5s model has better overall performance in terms of detection accuracy and speed. This study can provide a reference for real-time detection of breakage and mildew kernels during the harvesting process of corn kernels.

MOD-YOLO: Multispectral object detection based on transformer dual-stream YOLO

Multispectral object detection can effectively improve the precision of object detection in low-visibility scenes, which increases the reliability and stability of the object detection application in the open environment. Cross-Modality Fusion Transformer (CFT) can effectively fuse different spectral information, but this method relies on large models and expensive computing resources. In this paper, we propose multispectral object detection dual-stream YOLO (MOD-YOLO), based on Cross Stage Partial CFT (CSP-CFT), to address the issue that prior studies need heavy inference calculations from the recurrent fusing of multispectral features. This network can divide the fused feature map into two parts, respectively for cross stage output and combined with the next stage feature, to achieve the correct speed/memory/precision balance. To further improve the accuracy, SIoU was selected as the loss function. Ultimately, extensive experiments on multiple publicly available datasets demonstrate that our model, which achieves the smallest model size and excellent performance, produces better tradeoffs between accuracy and model size than other popular models.

Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems

In aerial surveillance systems, achieving optimal object detection precision is of paramount importance for effective monitoring and reconnaissance. This article presents a novel approach to enhance object detection accuracy through the integration of Delaunay triangulation with multi-unmanned aerial vehicle (UAV) systems. The methodology involves positioning multiple UAVs at pre-specified locations using the Delaunay triangulation algorithm with performance of O (n log n). This is compared with the conventional single UAV approach at a near distance. Our findings reveal that the collaborative efforts of multiple UAVs, guided by Delaunay triangulation, significantly improves object detection accuracy, especially when compared to a single UAV operating in close proximity. This research employs advanced image processing techniques to identify objects in the area under surveillance. Results indicate a substantial enhancement in the collective surveillance capabilities of the multi-UAV system, demonstrating its efficacy in unconstrained scenarios. This research not only contributes to the optimization of aerial surveillance operations but also underscores the potential of spatially informed UAV networks for applications demanding heightened object detection accuracy. The integration of Delaunay triangulation with multi-UAV systems emerges as a promising strategy for advancing the capabilities of aerial surveillance in scenarios ranging from security and emergency response to environmental monitoring.

Enhancing object detection in low-resolution images via frequency domain learning

To meet the requirements of navigation devices in terms of weight, power consumption, and size, it is necessary to capture low-resolution images or transmit low-resolution images to a server for object detection. However, due to the lack of details and frequency information, even state-of-the-art detection methods face challenges in accurately identifying objects. To tackle this issue, we introduce a novel upsampling method termed multi-wave representation upsampling, accompanied by a training strategy aimed at reinstating high-frequency details and augmenting the precision of object detection. Finally, we conduct empirical experiments showing that compared to alternative methodologies, our proposed approach yields images exhibiting minimal disparities in frequency compared to high-resolution counterparts. Additionally, it exhibits superior performance across objects of varying scales, while simultaneously demonstrating reduced parameter count and enhanced computational efficiency.

Boosting the Response of Object Detection and Steering Angle Prediction for Self-Driving Control

Our previous work introduced the LW-YOLOv4-tiny and the LW-ResNet18 models by replacing traditional convolution with the Ghost Conv to achieve rapid object detection and steering angle prediction, respectively. However, the entire object detection and steering angle prediction process has encountered a speed limit problem. Therefore, this study aims to significantly speed up the object detection and the steering angle prediction simultaneously. This paper proposes the GhostBottleneck approach to speed the frame rate of feature extraction and add the SElayer method to maintain the existing precision of object detection, which constructs an enhanced object detection model abbreviated as LWGSE-YOLOv4-tiny. In addition, this paper also conducted depthwise separable convolution to simplify the Ghost Conv as depthwise separable and ghost convolution, which constructs an improved steering angle prediction model abbreviated as LWDSG-ResNet18 that can considerably speed up the prediction and slightly increase image recognition accuracy. Compared with our previous work, the proposed approach shows that the GhostBottleneck module can significantly boost the frame rate of feature extraction by 9.98%, and SElayer can upgrade the precision of object detection slightly by 0.41%. Moreover, depthwise separable and ghost convolution can considerably boost prediction speed by 20.55% and increase image recognition accuracy by 2.05%.

An effective method for small object detection in low-resolution images

Having a tiny scale and few identifiable features, small objects are particularly difficult to detect, especially when the image resolution is not high. Further, large-scale variation across object instances places a burden on small object detection. In view of the insufficient detection ability of YOLOF (You Only Look One-level Feature) detector for small objects at low resolution, we propose an effective method, named YOLOFs, to boost small object detection precision in the case of object instances scale variation. The proposed three key modules (feature fusion module, visual perception module and feature encoding module) expand the receptive field of the network and greatly improve small object detection precision. Extensive experiments on the COCO, VOC, and Fire datasets prove the effectiveness of our method while keeping the detector simple and accurate. Without bells and whistles, our method outperforms YOLOF 5.1 AP (average precision), 6.2 AP, and 4.9 AP on small object detection at 704×704,608×608, and 512 × 512 resolutions, respectively, and it also achieves better performance on the VOC and Fire datasets. Our code will be made publicly available.

YOLO-G: Improved YOLO for cross-domain object detection.

Cross-domain object detection is a key problem in the research of intelligent detection models. Different from lots of improved algorithms based on two-stage detection models, we try another way. A simple and efficient one-stage model is introduced in this paper, comprehensively considering the inference efficiency and detection precision, and expanding the scope of undertaking cross-domain object detection problems. We name this gradient reverse layer-based model YOLO-G, which greatly improves the object detection precision in cross-domain scenarios. Specifically, we add a feature alignment branch following the backbone, where the gradient reverse layer and a classifier are attached. With only a small increase in computational, the performance is higher enhanced. Experiments such as Cityscapes→Foggy Cityscapes, SIM10k→Cityscape, PASCAL VOC→Clipart, and so on, indicate that compared with most state-of-the-art (SOTA) algorithms, the proposed model achieves much better mean Average Precision (mAP). Furthermore, ablation experiments were also performed on 4 components to confirm the reliability of the model. The project is available at https://github.com/airy975924806/yolo-G.

Neural adaptive IoT streaming analytics with RL-Adapt

IoT stream processing is an emerging technology that plays a crucial role in enabling time-critical IoT applications, which often demand high accuracy and low latency. For example, Augmented Reality (AR) applications require high object detection precision and object localization precision, as well as low latency. Note that the exact definition of accuracy varies depending on the specific application Existing stream processing engines are insufficient to meet these requirements, since they could not integrate and respond timely to variable network conditions in the dynamic wireless environment. Recent efforts focusing on adaptive streaming support user-specified policies to adapt to the variable network conditions. However, existing works suffer from manual policies or simplified models of the deployment environment which limit their ability to achieve optimal performance across a broad set of network conditions and quality of experience (QoE) objectives. In this paper, we present a Reinforcement Learning-based Adaptive streaming system (RL-Adapt) that is capable of generating adaption policies using RL-strategy and providing declarative APIs for efficient development. RL-Adapt trains a neural network model that can automatically select the optimal policy based on the observed network conditions. RL-Adapt does not rely on pre-defined models or assumptions about the environment. Instead, it learns to make decisions solely through observations of the resulting performance of past decisions. We implemented RL-Adapt and evaluated its performance extensively in three representative real-world IoT applications. Our results show that RL-Adapt outperforms the state-of-the-art schemes in terms of QoE metrics.

Object Detection Based on Fusion of Visible and Infrared Images

In consideration of the complementary characteristics between visible light and infrared images, this paper proposes a novel method for object detection based on the fusion of these two types of images, thereby enhancing detection accuracy even under harsh environmental conditions. Specifically, we employ an improved AE network, which encodes and decodes the visible light and infrared images into dual-scale image decomposition. By reconstructing the original images with the decoder, we highlight the details of the fused image. Yolov5 network is then constructed based on this fused image, and its parameters are adjusted accordingly to achieve accurate detection of objects. Due to the complementary information features that are missing between the two image types, our method effectively enhances the precision of object detection.

An Improved YOLO Model for Traffic Signs Small Target Image Detection

Traffic sign detection significantly reduces traffic accidents, but in real-world scenarios, the considerable distance between traffic signs and in-vehicle cameras means only a small proportion of traffic signs are captured in the images. This makes far-off traffic sign detection a small object detection problem, and with fewer details in small sign objects, detection becomes increasingly challenging. In this paper, we specifically address poor localization, low accuracy, and missed detections when using You Only Look Once Version 5 (YOLOv5) for detecting small traffic sign objects. Firstly, we incorporate a decoupled head into YOLOv5’s detection algorithm, which serves to improve detection accuracy and accelerate network convergence. Then, to handle low-resolution targets better, we substitute the network’s original convolution layers with Space-to-Depth Convolution (SPD-Conv) modules. This modification enhances the model’s capacity to extract features from low-resolution traffic sign objects. Lastly, we integrate the Context Augmentation Module (CAM) into YOLOv5 by employing variable rate extended convolution. This module extracts context information from multiple receptive fields, thus providing essential supplementary information and significantly enhancing detection accuracy. Empirical results demonstrate the efficacy of our algorithm, shown by a substantial increase in object detection precision rate to 95.0%, a recall rate of 91.6%, and an average precision of 95.4%. These results represent improvements of 2.1%, 4.8% and 3.7%, respectively, when compared to the original YOLOv5 algorithm. Furthermore, when tested against other state-of-the-art methods, our proposed methodology shows superior performance.

ResNet and Yolov5-enabled non-invasive meat identification for high-accuracy box label verification

Compliance issues riddle the agricultural sector despite being an essential industry for the human race. Many factors contribute to compliance issues; however, meat cut label verification is one of the most critical concerns due to its erroneous nature. In addition, meat cut identification is complex for the human eye. Thus, access to a skilled labor force is challenging. Nevertheless, meat compliance is essential since it has export compliance ramifications. These factors, along with others, are pushing for a digital alternative. An alternative that can augment human decision-making in verifying meat cut box labels. Artificial Intelligence (AI) is a digital alternative that can boost quality assurance tasks. One of AI’s potential quality assurance solutions is a Meat Box Labeling Verification (BLV) solution that can verify the boxed meat type against the label to ensure no mismatch. Two major components make up the BLV solution: Meat Identification and Label Analysis. This work aims to solve the former component by exploring different meat-type identification techniques. It explores them by building, evaluating, and testing different computer vision solutions. Hence, the novel contribution of this work is three folds. We first build, test, and design computer vision solutions that detect meat boxes and pieces accurately. These solutions include deep learning methods in classification and object detection techniques. Following that, we evaluate these models and derive key insights. Such insights are valuable for prototyping such solutions in production environments. For example, classification models achieve a 99% testing accuracy in identifying box types. In contrast, object detection achieved 89% mAP@.5:.95 in identified individual meat cuts. Finally, we prototype an object detection model in a natural meat processing environment—the demonstration showed comparable object detection precision at 85% mAP@.5:.95.

An object detection algorithm combining self-attention and YOLOv4 in traffic scene.

Automobile intelligence is the trend for modern automobiles, of which environment perception is the key technology of intelligent automobile research. For autonomous vehicles, the detection of object information, such as vehicles and pedestrians in traffic scenes is crucial to improving driving safety. However, in the actual traffic scene, there are many special conditions such as object occlusion, small objects, and bad weather, which will affect the accuracy of object detection. In this research, the SwinT-YOLOv4 algorithm is proposed for detecting objects in traffic scenes, which is based on the YOLOv4 algorithm. Compared with a Convolutional neural network (CNN), the vision transformer is more powerful at extracting vision features of objects in the image. The CNN-based backbone in YOLOv4 is replaced by the Swin Transformer in the proposed algorithm. The feature-fusing neck and predicting head of YOLOv4 is remained. The proposed model was trained and evaluated in the COCO dataset. Experiments show that our method can significantly improve the accuracy of object detection under special conditions. Equipped with our method, the object detection precision for cars and person is improved by 1.75%, and the detection precision for car and person reach 89.04% and 94.16%, respectively.

A multitask model for realtime fish detection and segmentation based on YOLOv5.

The accuracy of fish farming and real-time monitoring are essential to the development of "intelligent" fish farming. Although the existing instance segmentation networks (such as Maskrcnn) can detect and segment the fish, most of them are not effective in real-time monitoring. In order to improve the accuracy of fish image segmentation and promote the accurate and intelligent development of fish farming industry, this article uses YOLOv5 as the backbone network and object detection branch, combined with semantic segmentation head for real-time fish detection and segmentation. The experiments show that the object detection precision can reach 95.4% and the semantic segmentation accuracy can reach 98.5% with the algorithm structure proposed in this article, based on the golden crucian carp dataset, and 116.6 FPS can be achieved on RTX3060. On the publicly available dataset PASCAL VOC 2007, the object detection precision is 73.8%, the semantic segmentation accuracy is 84.3%, and the speed is up to 120 FPS on RTX3060.

Fish sonar image recognition algorithm based on improved YOLOv5.

Fish stock assessment is crucial for sustainable marine fisheries management in rangeland ecosystems. To address the challenges posed by the overfishing of offshore fish species and facilitate comprehensive deep-sea resource evaluation, this paper introduces an improved fish sonar image detection algorithm based on the you only look once algorithm, version 5 (YOLOv5). Sonar image noise often results in blurred targets and indistinct features, thereby reducing the precision of object detection. Thus, a C3N module is incorporated into the neck component, where depth-separable convolution and an inverse bottleneck layer structure are integrated to lessen feature information loss during downsampling and forward propagation. Furthermore, lowercase shallow feature layer is introduced in the network prediction layer to enhance feature extraction for pixels larger than $ 4 \times 4 $. Additionally, normalized weighted distance based on a Gaussian distribution is combined with Intersection over Union (IoU) during gradient descent to improve small target detection and mitigate the IoU's scale sensitivity. Finally, traditional non-maximum suppression (NMS) is replaced with soft-NMS, reducing missed detections due to occlusion and overlapping fish targets that are common in sonar datasets. Experiments show that the improved model surpasses the original model and YOLOv3 with gains in precision, recall and mean average precision of 2.3%, 4.7% and 2.7%, respectively, and 2.5%, 6.3% and 6.7%, respectively. These findings confirm the method's effectiveness in raising sonar image detection accuracy, which is consistent with model comparisons. Given Unmanned Underwater Vehicle advancements, this method holds the potential to support fish culture decision-making and facilitate fish stock resource assessment.

Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN.

With the development of neural networks, object detection based on deep learning is developing rapidly, and its applications are gradually increasing. In the tire industry, detecting speckle interference bubble defects of tire crown has difficulties such as low image contrast, small object scale, and large internal differences of defects, which affect the detection precision. To solve these problems, we propose a new feature pyramid network based on Faster RCNN-FPN. It can fuse features across levels and directions to improve small object detection and localization, and increase object detection precision. The method has proven its effectiveness through cross-validation experiments. On a tire crown bubble defect dataset, the mAP [0.5:0.95] increased by 2.08% and the AP0.5 increased by 2.4% over the original network. The results show that the improved network significantly improves detecting tire crown bubble defects.