Real-time Detection Performance Research Articles

An in-vehicle real-time infrared object detection system based on deep learning with resource-constrained hardware

Advanced driver assistance systems primarily rely on visible images for information. However, in low-visibility weather conditions, such as heavy rain or fog, visible images struggle to capture road conditions accurately. In contrast, infrared (IR) images can overcome this limitation, providing reliable information regardless of external lighting. Addressing this problem, we propose an in-vehicle IR object detection system. We optimize the you only look once (YOLO) v4 object detection algorithm by replacing its original backbone with MobileNetV3, a lightweight feature extraction network, resulting in the MobileNetV3-YOLOv4 model. Furthermore, we replace traditional pre-processing methods with an Image Enhancement Conditional Generative Adversarial Network inversion algorithm to enhance the pre-processing of the input IR images. Finally, we deploy the model on the Jetson Nano, an edge device with constrained hardware resources. Our proposed method achieves an 82.7% mean Average Precision and a frame rate of 55.9 frames per second on the FLIR dataset, surpassing state-of-the-art methods. The experimental results confirm that our approach provides outstanding real-time detection performance while maintaining high precision.

IV-YOLO: A Lightweight Dual-Branch Object Detection Network

With the rapid growth in demand for security surveillance, assisted driving, and remote sensing, object detection networks with robust environmental perception and high detection accuracy have become a research focus. However, single-modality image detection technologies face limitations in environmental adaptability, often affected by factors such as lighting conditions, fog, rain, and obstacles like vegetation, leading to information loss and reduced detection accuracy. We propose an object detection network that integrates features from visible light and infrared images—IV-YOLO—to address these challenges. This network is based on YOLOv8 (You Only Look Once v8) and employs a dual-branch fusion structure that leverages the complementary features of infrared and visible light images for target detection. We designed a Bidirectional Pyramid Feature Fusion structure (Bi-Fusion) to effectively integrate multimodal features, reducing errors from feature redundancy and extracting fine-grained features for small object detection. Additionally, we developed a Shuffle-SPP structure that combines channel and spatial attention to enhance the focus on deep features and extract richer information through upsampling. Regarding model optimization, we designed a loss function tailored for multi-scale object detection, accelerating the convergence speed of the network during training. Compared with the current state-of-the-art Dual-YOLO model, IV-YOLO achieves mAP improvements of 2.8%, 1.1%, and 2.2% on the Drone Vehicle, FLIR, and KAIST datasets, respectively. On the Drone Vehicle and FLIR datasets, IV-YOLO has a parameter count of 4.31 M and achieves a frame rate of 203.2 fps, significantly outperforming YOLOv8n (5.92 M parameters, 188.6 fps on the Drone Vehicle dataset) and YOLO-FIR (7.1 M parameters, 83.3 fps on the FLIR dataset), which had previously achieved the best performance on these datasets. This demonstrates that IV-YOLO achieves higher real-time detection performance while maintaining lower parameter complexity, making it highly promising for applications in autonomous driving, public safety, and beyond.

Defect detection of printed circuit board assembly based on YOLOv5

Detection of printed circuit board assembly (PCBA) defects is crucial for improving the efficiency of PCBA manufacturing. This paper proposes PCBA-YOLO, a YOLOv5-based method that can effectively increase the accuracy of PCBA defect detection. First, the spatial pyramid pooling module with cross-stage partial structure is replaced in the neck network of YOLOv5 to capture the resolution features at multiple scales. Second, large kernel convolution is introduced in the backbone network to obtain larger effective receptive fields while reducing computational overhead. Finally, an SIoU loss function that considers the angular cost is utilized to enhance the convergence speed of the model. In addition, an assembled PCBA defect detection dataset named PCBA-DET is created in this paper, containing the corresponding defect categories and annotations of defect locations. The experimental results on the PCB defect dataset demonstrate that the improved method has lower loss values and higher accuracy. Evaluated on the PCBA-DET dataset, the mean average precision reaches 97.3%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, achieving a real-time detection performance of 322.6 frames per second, which considers both the detection accuracy and the model size compared to the YOLO series of detection networks. The source code and PCBA-DET dataset can be accessed at https://github.com/ismh16/PCBA-Dataset.

On-Line Detection Method of Salted Egg Yolks with Impurities Based on Improved YOLOv7 Combined with DeepSORT.

Salted duck egg yolk, a key ingredient in various specialty foods in China, frequently contains broken eggshell fragments embedded in the yolk due to high-speed shell-breaking processes, which pose significant food safety risks. This paper presents an online detection method, YOLOv7-SEY-DeepSORT (salted egg yolk, SEY), designed to integrate an enhanced YOLOv7 with DeepSORT for real-time and accurate identification of salted egg yolks with impurities on production lines. The proposed method utilizes YOLOv7 as the core network, incorporating multiple Coordinate Attention (CA) modules in its Neck section to enhance the extraction of subtle eggshell impurities. To address the impact of imbalanced sample proportions on detection accuracy, the Focal-EIoU loss function is employed, adaptively adjusting bounding box loss values to ensure precise localization of yolks with impurities in images. The backbone network is replaced with the lightweight MobileOne neural network to reduce model parameters and improve real-time detection performance. DeepSORT is used for matching and tracking yolk targets across frames, accommodating rotational variations. Experimental results demonstrate that YOLOv7-SEY-DeepSORT achieves a mean average precision (mAP) of 0.931, reflecting a 0.53% improvement over the original YOLOv7. The method also shows enhanced tracking performance, with Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) scores of 87.9% and 73.8%, respectively, representing increases of 17.0% and 9.8% over SORT and 2.9% and 4.7% over Tracktor. Overall, the proposed method balances high detection accuracy with real-time performance, surpassing other mainstream object detection methods in comprehensive performance. Thus, it provides a robust solution for the rapid and accurate detection of defective salted egg yolks and offers a technical foundation and reference for future research on the automated and safe processing of egg products.

Agricultural object detection with You Only Look Once (YOLO) Algorithm: A bibliometric and systematic literature review

Vision is a major component in several digital technologies and tools used in agriculture. Object detection plays a pivotal role in digital farming by automating the task of detecting, identifying, and localization of various objects in large-scale agrarian landscapes. The single-stage detection algorithm, You Only Look Once (YOLO), has gained popularity in agriculture in a relatively short span due to its state-of-the-art performance in terms of accuracy, speed, and network size. YOLO offers real-time detection performance with good accuracy and is implemented in various agricultural tasks, including monitoring, surveillance, sensing, automation, and robotics operations. The research and application of YOLO in agriculture are accelerating at a tremendous speed but are fragmented and multidisciplinary in nature. Moreover, the performance characteristics (i.e., accuracy, speed, computation) of the object detector influence the rate of technology implementation and adoption in agriculture. Therefore, this study aimed to collect extensive literature to document and critically evaluate the advances and application of YOLO for agricultural object recognition tasks. First, we conducted a bibliometric review of 257 selected articles to understand the scholarly landscape (i.e., research trends, evolution, global hotspots, and gaps) of YOLO in the broad agricultural domain. Secondly, we conducted a systematic literature review on 30 selected articles to identify current knowledge, critical gaps, and modifications in YOLO for specific agricultural tasks. The study critically assessed and summarized the information on YOLO’s end-to-end learning approach, including data acquisition, processing, network modification, integration, and deployment. We also discussed task-specific YOLO algorithm modification and integration to meet the agricultural object or environment-specific challenges. In general, YOLO-integrated digital tools and technologies showed the potential for real-time, automated monitoring, surveillance, and object handling to reduce labor, production cost, and environmental impact while maximizing resource efficiency. The study provides detailed documentation and significantly advances the existing knowledge on applying YOLO in agriculture, which can greatly benefit the scientific community. The results of this study open the door for implementing YOLO-based solutions in practical agricultural scenarios and add to the expanding corpus of information on computer vision applications in agriculture.

An Improved Face Mask Detection Simulation Algorithm Based on YOLOv5 Model

This paper proposes an advanced approach for detecting faces with mask occlusion based on YOLOv5 to address various challenges encountered in face detection, including illumination blur and occlusion. The proposed methodology involves the integration of a convolutional block attention module into the backbone network and different network levels in the neck of the YOLOv5s. This approach enables the suppression of irrelevant features and emphasizes the identification of masked facial features. Replacing the conventional loss function with the Focal Loss function addresses the problem of sample imbalance. The enhanced YOLOv5s network was applied to detect mask-occluded faces. Empirical evaluations were conducted on the WIDER Face and AIZOO datasets. The simulation comparison results demonstrate that the proposed method achieves superior real-time detection performance, fulfilling the objective of developing a lightweight detection model.

RSR-YOLO: a real-time method for small target tomato detection based on improved YOLOv8 network

In tomato producing fields, automated large-area detection method is critical for fruit health monitoring and harvesting. However, due to the limited feature information included in tomatoes, large-area detection across long distances results in more missing or incorrect detections. To address this issue, this research proposes an improved YOLOv8 network, RSR-YOLO, for long-distance identification of tomato fruits. Firstly, this paper designs a partial group convolution (PgConv) and furthermore an innovative FasterNet (IFN) module for feature extraction, taking into account the impact of split operations on the computational complexity of the backbone network. The IFN module is lightweight and efficient, which improves the detection accuracy and real-time detection performance of the model. Secondly, this research combines the Gather and Distribute mechanism (GD) and redesigns the feature fusion module to implement the extraction and fusion of various levels of tomato features, given the critical significance that low-dimensional features play in small target recognition and localization. Finally, Repulsion Loss is used in this paper to examine the impact of fruit overlap and leaf occlusion on detection outcomes. RSR-YOLO achieves precision, recall, F1 score, and mean average precision (mAP@0.5) of 91.6%, 85.9%, 88.7%, and 90.7%, respectively, marking increases of 4.2%, 4%, 4.2%, and 3.6% compared to YOLOv8n. In addition, this paper designs a specialized Graphical User Interface (GUI) for the real-time detection task of tomatoes.

Real-Time Recognition Algorithm of Small Target for UAV Infrared Detection

Unmanned Aerial Vehicle (UAV) infrared detection has problems such as weak and small targets, complex backgrounds, and poor real-time detection performance. It is difficult for general target detection algorithms to achieve the requirements of a high detection rate, low missed detection rate, and high real-time performance. In order to solve these problems, this paper proposes an improved small target detection method based on Picodet. First, to address the problem of poor real-time performance, an improved lightweight LCNet network was introduced as the backbone network for feature extraction. Secondly, in order to solve the problems of high false detection rate and missed detection rate due to weak targets, the Squeeze-and-Excitation module was added and the feature pyramid structure was improved. Experimental results obtained on the HIT-UAV public dataset show that the improved detection model’s real-time frame rate increased by 31 fps and the average accuracy (MAP) increased by 7%, which proves the effectiveness of this method for UAV infrared small target detection.

Establishment and validation of an artificial intelligence-based model for real-time detection and classification of colorectal adenoma

Colorectal cancer (CRC) prevention requires early detection and removal of adenomas. We aimed to develop a computational model for real-time detection and classification of colorectal adenoma. Computationally constrained background based on real-time detection, we propose an improved adaptive lightweight ensemble model for real-time detection and classification of adenomas and other polyps. Firstly, we devised an adaptive lightweight network modification and effective training strategy to diminish the computational requirements for real-time detection. Secondly, by integrating the adaptive lightweight YOLOv4 with the single shot multibox detector network, we established the adaptive small object detection ensemble (ASODE) model, which enhances the precision of detecting target polyps without significantly increasing the model's memory footprint. We conducted simulated training using clinical colonoscopy images and videos to validate the method's performance, extracting features from 1148 polyps and employing a confidence threshold of 0.5 to filter out low-confidence sample predictions. Finally, compared to state-of-the-art models, our ASODE model demonstrated superior performance. In the test set, the sensitivity of images and videos reached 87.96% and 92.31%, respectively. Additionally, the ASODE model achieved an accuracy of 92.70% for adenoma detection with a false positive rate of 8.18%. Training results indicate the effectiveness of our method in classifying small polyps. Our model exhibits remarkable performance in real-time detection of colorectal adenomas, serving as a reliable tool for assisting endoscopists.

Fine-Grained Feature Perception for Unmanned Aerial Vehicle Target Detection Algorithm

Unmanned aerial vehicle (UAV) aerial images often present challenges such as small target sizes, high target density, varied shooting angles, and dynamic poses. Existing target detection algorithms exhibit a noticeable performance decline when confronted with UAV aerial images compared to general scenes. This paper proposes an outstanding small target detection algorithm for UAVs, named Fine-Grained Feature Perception YOLOv8s-P2 (FGFP-YOLOv8s-P2), based on YOLOv8s-P2 architecture. We specialize in improving inspection accuracy while meeting real-time inspection requirements. First, we enhance the targets’ pixel information by utilizing slice-assisted training and inference techniques, thereby reducing missed detections. Then, we propose a feature extraction module with deformable convolutions. Decoupling the learning process of offset and modulation scalar enables better adaptation to variations in the size and shape of diverse targets. In addition, we introduce a large kernel spatial pyramid pooling module. By cascading convolutions, we leverage the advantages of large kernels to flexibly adjust the model’s attention to various regions of high-level feature maps, better adapting to complex visual scenes and circumventing the cost drawbacks associated with large kernels. To match the excellent real-time detection performance of the baseline model, we propose an improved Random FasterNet Block. This block introduces randomness during convolution and captures spatial features of non-linear transformation channels, enriching feature representations and enhancing model efficiency. Extensive experiments and comprehensive evaluations on the VisDrone2019 and DOTA-v1.0 datasets demonstrate the effectiveness of FGFP-YOLOv8s-P2. This achievement provides robust technical support for efficient small target detection by UAVs in complex scenarios.

DeepVision based detection for energy-efficiency and indoor air quality enhancement in highly polluted spaces

Cooking can generate substantial heat from cooking equipment, potentially resulting in reduced thermal comfort levels if this excess heat is not adequately dissipated. Additionally, it can significantly affect indoor air quality (IAQ), not only in kitchen spaces but also in adjacent areas lacking sufficient ventilation. To ensure a healthy and comfortable indoor environment while avoiding unnecessary energy use, this research proposes an approach for detecting equipment usage. Utilizing deep learning and computer vision techniques (referred to as DeepVision), this method aids the operation of demand-driven ventilation systems in highly polluted spaces. A Faster RCNN model was employed and trained to detect kitchen equipment usage in real-time. The ventilation rate for the kitchen space was adjusted based on this real-time detection. Experimental tests were carried out in a case study kitchen and results showed that the detection model achieved an overall F1 score of 0.9142. Overall, the model achieved good performance in real-time detection, accurately identifying appliances in use, such as stoves, ovens, and toasters. Field experimental results showed the advantages of combining mechanical ventilation methods, such as extractor fans and cooker hoods, to mitigate IAQ issues while also achieving energy savings. Moreover, the energy simulations demonstrate its potential to reduce energy consumption by dynamically adjusting ventilation rates in response to real-time equipment usage. When the fan speed was varied according to the real-time detection method, the PM2.5 concentration in the cooking period was 16.5 % lower, with only a 3.7 % rise in fan power and a 0.8 % rise in daily heating demand, compared to using a constant fan speed.

Lightweight coal and gangue detection algorithm based on improved Yolov7-tiny

ABSTRACT This paper proposes a lightweight coal and gangue detection algorithm based on Yolov7-tiny for the current target detection algorithms, which have problems such as large model computation, low detection accuracy and low real-time detection performance. Firstly, we make cuts for the ShuffleNetV2 network and replace the Stage1 and Stage2 layers with CONV convolutional blocks to construct a new backbone network that improves the speed of feature extraction. Secondly, the neck of the original model is redesigned by fusing the original two paths of up-sampling and down-sampling into one path, introducing the CBAM attention mechanism to further improve the information extraction, fusion and interaction capabilities of the model, and the detection heads are reduced from three to two to improve the real-time inference capability of the model. Finally, SIoU is used to replace the loss function of the original network to improve the convergence ability and robustness of the model. The experimental results show that the accuracy and FPS of the lightweight model proposed in this paper reach 0.981 and 208, respectively. The improved model has higher real-time and accuracy, and the reduction of computation provides the possibility of deploying it on the edge devices.

An effective MLP model for detecting malicious nodes in PoS permissionless blockchains

With the proliferation of blockchain technology, ensuring the security and integrity of permissionless Proof-of-Stake (PoS) blockchain networks has become imperative. This paper addresses the persistent need for an effective system to detect and mitigate malicious nodes in such environments. Leveraging Deep Learning (DL) techniques, specifically Multi-Layer Perceptron (MLP), a novel model is proposed for real-time identification and detection of malicious nodes in PoS blockchain networks. The model integrates components for data collection, feature extraction, and model training using MLP. The proposed model is trained on labelled data representing both benign and malicious node activities, utilising transaction volumes, frequencies, timestamps, and node reputation scores to identify anomalous behaviour indicative of malicious activity. The experimental results validate the efficacy of the proposed model in distinguishing between normal and malicious nodes within blockchain networks. The model demonstrates exceptional performance in classification tasks with an accuracy of 99%, precision, recall, and F1-score values hovering around 0.99 for both classes. The experimental results verify the proposed model as a dependable tool for enhancing the security and integrity of PoS blockchain networks, offering superior performance in real-time detection and mitigation of malicious activities.

A Reconfigurable Near-Sensor Processor for Anomaly Detection in Limb Prostheses.

This paper presents a reconfigurable near-sensor anomaly detection processor to real-time monitor the potential anomalous behaviors of amputees with limb prostheses. The processor is low-power, low-latency, and suitable for equipment on the prostheses and comprises a reconfigurable Variational Autoencoder (VAE), a scalable Self-Organizing Map (SOM) Array, and a window-size-adjustable Markov Chain, which can implement an integrated miniaturized anomaly detection system. With the reconfigurable VAE, the proposed processor can support up to 64 sensor sampling channels programmable by global configuration, which can meet the anomaly detection requirements in different scenarios. A scalable SOM array allows for the selection of different sizes based on the complexity of the data. Unlike traditional time accumulation-based anomaly detection methods, the Markov Chain is utilized to detect time-series-based anomalous data. The processor is designed and fabricated in a UMC 40-nm LP technology with a core area of 1.49 mm2 and a power consumption of 1.81 mW. It achieves real-time detection performance with 0.933 average F1 Score for the FSP dataset within 24.22 μs, and 0.956 average F1 Score for the SFDLA-12 dataset within 30.48 μs, respectively. The energy dissipation of detection for each input feature is 43.84 nJ with the FSP dataset, and 55.17 nJ with the SFDLA-12 dataset. Compared with ARM Cortex-M4 and ARM Cortex-M33 microcontrollers, the processor achieves energy and area efficiency improvements ranging from 257×, 193× and 11×, 8×, respectively.

YOLO_MRC: A fast and lightweight model for real-time detection and individual counting of Tephritidae pests

Tephritidae pests severely affect the quality and safety of various melons, fruits and vegetable crops. However, many agricultural managers lack an adequate understanding of the level of pest occurrence, resulting in the misuse of pesticides, which ultimately leads to environmental pollution and economic loss. Therefore, real-time detection and counting of Tephritidae pests are important for timely pest spotting and control. This work helps quickly determine the distribution and abundance of pests in the current environment, thus providing data on pest conditions for agricultural management to optimize pesticide use. Nevertheless, the fast speed, high accuracy, and lightweight performance of real-time detection and counting are difficult to balance. To address this problem, based on the YOLOv8n model, this paper takes Bactrocera cucurbitae pests as the detection target and proposes a fast and lightweight real-time detection and individual counting model for Tephritidae pests, named YOLO_MRC. This paper introduces three key improvements: (1) Constructing a new module called Multicat into the neck network increases the focus on the detection target by incorporating an attention mechanism; (2) Reducing the original three detection heads to two and then adjusting their sizes to decrease the number of parameters in the network model; (3) Devising a novel module, C2flite, to enhance the deep feature extraction capability of the backbone network. According to the above points, this paper conducts ablation experiments to compare the performances of different models. Experiments showed that the Multicat module significantly offsets the large increase in GFLOPs and processing time caused by reducing the detection head and can further reduce the number of parameters and improve the accuracy when combined with the C2flite module. On our Bactrocera cucurbitae pest dataset, the mAP@0.5 of the YOLO_MRC model reached 99.3%. Simultaneously, as the number of parameters decreases by 63.68%, GFLOPs is reduced by 19.75%, and the processing time is shortened by 5%. To ensure the validity of the model, YOLO_MRC is compared with four excellent detection models by using manual counting results as the benchmark. YOLO_MRC achieves an average pest counting accuracy of 94%, demonstrating superior performance in terms of model size and processing time. To further explore the performance of YOLO_MRC in multiclass insect detection tasks, we choose the public dataset Pest_24_640 for comparison with four state-of-the-art models. YOLO_MRC achieves a 3.6 ms processing time and 70.4% accuracy with only a 2.4 MB model size, which demonstrates the potential of YOLO_MRC in multiclass pest detection.

An improved YOLO algorithm for detecting flowers and fruits on strawberry seedlings

Accurately identifying the flowers and fruits of strawberry seedlings in the greenhouse is the key to automated flower and fruit thinning, which can improve efficiency and reduce labour costs in the cultivation. To address the challenges resulting from the small size, similar colour, and overlapping occlusion of strawberry seedling flowers and fruits, this paper proposes a real-time recognition algorithm (Improved YOLO) for accurately identifying them. Firstly, a Swin Transformer prediction head on the high-resolution feature map of YOLO v7 was constructed to better utilise spatial location information to enhance the detection of small target flowers and fruits, and improve the model's spatial interaction and feature extraction ability in scenes with similar colours and overlapping occlusions. Secondly, the GS-ELAN Optimisation Module for neck of network by GSConv was constructed to suppress shallow noise interference from the high-resolution prediction head and mitigate the increase of parameters resulting from high-resolution prediction heads. The experimental results showed that the Precision(P), Recall(R), and mean Average Precision (mAP) of Improved YOLO are 92.6%, 89.6%, and 92.1%. In the meantime, the Improved YOLO algorithm achieves a frame rate of 45 f/s, satisfying the real-time detection requirements. It is 3.2%, 2.7%, and 4.6% higher than the precision, recall, and mAP of YOLOv7, respectively. The accuracy of this model for detecting flowers and fruits was 93.9% and 91.3%, the recall was 93% and 86.3%, and the average precision was 94.7% and 89.5%, respectively. The Improved YOLO algorithm has a high level of robustness and real-time detection performance, allowing it to quickly and accurately identify the flowers and fruits of strawberry seedlings and provides effective support for the automated management of flower and fruit thinning of strawberry seedlings in greenhouse environments.

Single-atom Cu-attached MOFs as peroxide-like enzymes to construct dual-mode immunosensors for detection of breast cancer typing in serum

Molecular subtype of breast cancer has defined the treatment direction of breast cancer and guided the selection of drugs. Invasive tests such as tissue biopsy have the potential to promote the metastasis of cancer cells. Therefore, the continuous development of high-performance and low-cost in vitro diagnostic tools is crucial for the diagnosis and treatment of cancer prognosis. Liquid biopsy techniques provide noninvasive, real-time, dynamic, multicomponent, quantitative, and long-term observations at the cellular, genetic, and molecular levels in vivo. A Cu-Zr metal-organic framework(MOF) nanomases with monatomic Cu attachment has been synthesized by covalently attaching single atom Cu to the defect site of zirconia clusters in the UiO-66. The catalytic performance of Cu/UiO-66 as a peroxide-like enzyme was verified by enzyme kinetics calculation. Using o-phenylenediamine as substrate, a colorimetric fluorescence dual-mode immunosensor was constructed by introducing blue fluorescent carbon quantum dots (CDs) based on ultraviolet chromogenic reaction and fluorescence internal filtration effect. By magnetic adsorption separation, the background signal of the sensor is reduced and the sensitivity of the sensor is further improved. In 0.001–1 ng/mL detection range, detection of HER2/ER/PR/Ki-67, the result has obvious linear relationship. Among them, the colorimetric detection limit were 0.367/0.379/0.392/0.388 pg/mL; the ratio fluorescence detection limit can reach 0.3, 0.289, 0.257, 0.33 pg/mL, with high sensitivity. The proteins HER2, ER, PR and Ki-67 in serum related to human breast cancer typing were detected with high sensitivity. Finally, the actual serum samples were tested using sensors. According to the confirmed detection criteria, the classification of serum breast cancer was preliminarily explored with an accuracy of 87.5 %. The sensor has high catalytic performance and excellent real-time detection performance of biomarkers, and have practical application potential in human serum level detection. It provides a new method for the classification of breast cancer in serum.

Efficient YOLOv7-Drone: An Enhanced Object Detection Approach for Drone Aerial Imagery

In recent years, the rise of low-cost mini rotary-wing drone technology across diverse sectors has emphasized the crucial role of object detection within drone aerial imagery. Low-cost mini rotary-wing drones come with intrinsic limitations, especially in computational power. Drones come with intrinsic limitations, especially in resource availability. This context underscores an urgent need for solutions that synergize low latency, high precision, and computational efficiency. Previous methodologies have primarily depended on high-resolution images, leading to considerable computational burdens. To enhance the efficiency and accuracy of object detection in drone aerial images, and building on the YOLOv7, we propose the Efficient YOLOv7-Drone. Recognizing the common presence of small objects in aerial imagery, we eliminated the less efficient P5 detection head and incorporated the P2 detection head for increased precision in small object detection. To ensure efficient feature relay from the Backbone to the Neck, channels within the CBS module were optimized. To focus the model more on the foreground and reduce redundant computations, the TGM-CESC module was introduced, achieving the generation of pixel-level constrained sparse convolution masks. Furthermore, to mitigate potential data losses from sparse convolution, we embedded the head context-enhanced method (HCEM). Comprehensive evaluation using the VisDrone and UAVDT datasets demonstrated our model’s efficacy and practical applicability. The Efficient Yolov7-Drone achieved state-of-the-art scores while ensuring real-time detection performance.

A remote sensing image rotation object detection approach for real-time environmental monitoring

For the problems of resources and environment faced in sustainable development, the use of remote sensing image object detection technology is an effective means to detect and analyze this major problem. Aiming at the problems of low accuracy and slow detection speed of existing remote sensing image rotation object detection algorithms implemented based on Transformer, a remote sensing image rotation object detection method based on dynamic position information Transformer is proposed. Firstly, to improve the detection accuracy, the cross-attention operation of the decoder is improved, and the obtained results are iteratively updated with the position information of the object queries and used as the initial object queries for the next decoder; secondly, to improve the robustness of the network for remote sensing image object detection, the data processing method of image pyramid is designed; Finally, a rotating IoU matching loss function more suitable for oriented object detection is introduced to improve the accuracy of matching the predicted boxed to the true boxed. The detection algorithm proposed in this paper is experimentally verified on DOTA and SSDD datasets, and the average detection accuracy is 73.70% and 90.3%, respectively, which effectively improves the average detection accuracy of Transformer-based rotating object detection algorithm in aerial remote sensing images and has better real-time detection performance.

MagicCubePose, A more comprehensive 6D pose estimation network

Most of the current mainstream 6D pose estimation methods use template or voting-based methods. Such methods are usually multi-stage or have multiple assumptions and post-correction, which will cause a certain degree of information redundancy and increase the computational cost, their real-time detection performance is poor. We point out that traditional path aggregation networks introduce new errors, therefore, we propose a loss function: MagicCubeLoss, a portable module: MagicCubeNet, and the corresponding 6D pose estimation model: MagicCubePose. MagicCubePose has good expansion performance and can build more efficient models for different calculation power and scenarios. Experiments show that our model has good real-time detection performance and the highest ADD(-S) accuracy.