Improves Mean Average Precision Research Articles

Z-YOLOv8s-based approach for road object recognition in complex traffic scenarios

Object detection in road scenarios is crucial for intelligent transport systems and autonomous driving, but complex traffic conditions pose significant challenges. This paper introduces Z-You Only Look Once version 8 small (Z-YOLOv8s), designed to improve both accuracy and real-time efficiency under real-world uncertainties. By incorporating Revisiting Perspective Vision Transformer (RepViT) and C2f into the YOLOv8s framework, and integrating the Large Selective Kernel Network (LSKNet), the model enhances spatial feature extraction. Additionally, the YOLOv8s backbone is optimized with Space-to-Depth Convolution (SPD-Conv) for better small object detection. The Softpool-Spatial Pyramid Pooling Fast (SoftPool-SPPF) module ensures precise characteristic information preservation. Z-YOLOv8s improves mean average precision (mAP)@0.5 on the Berkeley Deep Drive 100 K (BDD100K) and Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) datasets by 7.3 % and 3.8 %, respectively. It also achieves accuracy increases of 5.7 % and 6.5 % in Average Precision (AP)-Small, and a real-time detection speed of 78.41 frames per second (FPS) on the BDD100K. Z-YOLOv8s balances detection precision and processing speed more effectively than other detectors, as demonstrated by experimental results and comparisons.

A case study on decompounding in Indian language IR

Abstract Decompounding is an essential preprocessing step in text-processing tasks such as machine translation, speech recognition, and information retrieval (IR). Here, the IR issues are explored from five viewpoints. (A) Does word decompounding impact the Indian language IR? If yes, to what extent? (B) Can corpus-based decompounding models be used in the Indian language IR? If yes, how? (C) Can machine learning and deep learning-based decompounding models be applied in the Indian language IR? If yes, how? (D) Among the different decompounding models (corpus-based, hybrid machine learning-based, and deep learning-based), which provides the best effectiveness in the IR domain? (E) Among the different IR models, which provides the best effectiveness from the IR perspective? This study proposes different corpus-based, hybrid machine learning-based, and deep learning-based decompounding models in Indian languages (Marathi, Hindi, and Sanskrit). Moreover, we evaluate the effectiveness of each activity from an IR perspective only. It is observed that the different decompounding models improve IR effectiveness. The deep learning-based decompounding models outperform the corpus-based and hybrid machine learning-based models in Indian language IR. Among the different deep learning-based models, the Bi-LSTM-A model performs best and improves mean average precision (MAP) by 28.02% in Marathi. Similarly, the Bi-RNN-A model improves MAP by 18.18% and 6.1% in Hindi and Sanskrit, respectively. Among the retrieval models, the In_expC2 model outperforms others in Marathi and Hindi, and the BB2 model outperforms others in Sanskrit.

Improved senescent cell segmentation on bright‐field microscopy images exploiting representation level contrastive learning

AbstractMesenchymal stem cells (MSCs) are stromal cells which have multi‐lineage differentiation and self‐renewal potentials. Accurate estimation of total number of senescent cells in MSCs is crucial for clinical applications. Traditional manual cell counting using an optical bright‐field microscope is time‐consuming and needs an expert operator. In this study, the senescence cells were segmented and counted automatically by deep learning algorithms. However, well‐performing deep learning algorithms require large numbers of labeled datasets. The manual labeling is time consuming and needs an expert. This makes deep learning‐based automated counting process impractically expensive. To address this challenge, self‐supervised learning based approach was implemented. The approach incorporates representation level contrastive learning component into the instance segmentation algorithm for efficient senescent cell segmentation with limited labeled data. Test results showed that the proposed model improves mean average precision and mean average recall of downstream segmentation task by 8.3% and 3.4% compared to original segmentation model.

Enhancing Tuta absoluta Detection on Tomato Plants: Ensemble Techniques and Deep Learning

Early detection and efficient management practices to control Tuta absoluta (Meyrick) infestation is crucial for safeguarding tomato production yield and minimizing economic losses. This study investigates the detection of T. absoluta infestation on tomato plants using object detection models combined with ensemble techniques. Additionally, this study highlights the importance of utilizing a dataset captured in real settings in open-field and greenhouse environments to address the complexity of real-life challenges in object detection of plant health scenarios. The effectiveness of deep-learning-based models, including Faster R-CNN and RetinaNet, was evaluated in terms of detecting T. absoluta damage. The initial model evaluations revealed diminishing performance levels across various model configurations, including different backbones and heads. To enhance detection predictions and improve mean Average Precision (mAP) scores, ensemble techniques were applied such as Non-Maximum Suppression (NMS), Soft Non-Maximum Suppression (Soft NMS), Non-Maximum Weighted (NMW), and Weighted Boxes Fusion (WBF). The outcomes shown that the WBF technique significantly improved the mAP scores, resulting in a 20% improvement from 0.58 (max mAP from individual models) to 0.70. The results of this study contribute to the field of agricultural pest detection by emphasizing the potential of deep learning and ensemble techniques in improving the accuracy and reliability of object detection models.

DESPP-DETR: A Dense Connection Efficient Spatial Pooling DEtection TRansformer for Vehicle Detection

Real-time vehicle detection is a challenging and vital task in intelligent transportation systems. The key requirements for a vehicle detection model are speed and accuracy. However, existing real-time vehicle detection models often sacrifice one of these qualities in favor of the other. This trade-off makes them unfit for real-time deployment, where both speed and accuracy are equally important. Additionally, occlusion, which refers to the obstruction or partial covering of vehicles, further complicates detection and affects the system’s accuracy. In this study, we propose DESPP-DETR, a one-stage detection network for real-time vehicle detection. It is based on bipartite matching and a transformer encoder-decoder architecture, with the addition of a dense connection block and enhanced spatial pyramid pooling. The presence of dense connection block strengthens feature extraction. The enhanced spatial pyramid pooling eliminates the fixed-size constraint and increases the network’s learning capacity. When compared to existing models, DESPP-DETR achieves greater accuracy in real-time vehicle detection. On the MS COCO 2017 dataset, the proposed model achieves an improved mean average precision (mAP) of 75.53%, making it a promising solution for intelligent transportation systems.

Fault Detection in Power Distribution Networks Based on Comprehensive-YOLOv5.

Real-time fault detection in power distribution networks has become a popular issue in current power systems. However, the low power and computational capabilities of edge devices often fail to meet the requirements of real-time detection. To overcome these challenges, this paper proposes a lightweight algorithm, named Comprehensive-YOLOv5, for identifying defects in distribution networks. The proposed method focuses on achieving rapid localization and accurate identification of three common defects: insulator without loop, cable detachment from the insulator, and cable detachment from the spacer. Based on the You Only Look Once version 5 (YOLOv5) algorithm, this paper adopts GhostNet to reconstruct the original backbone of YOLOv5; introduces Bidirectional Feature Pyramid Network (BiFPN) structure to replace Path Aggregation Network (PANet) for feature fusion, which enhances the feature fusion ability; and replaces Generalized Intersection over Union GIOU with Focal Extended Intersection over Union (Focal-EIOU) to optimize the loss function, which improves the mean average precision and speed of the algorithm. The effectiveness of the improved Comprehensive-YOLOv5 algorithm is verified through a "morphological experiment", while an "algorithm comparison experiment" confirms its superiority over other algorithms. Compared with the original YOLOv5, the Comprehensive-YOLOv5 algorithm improves mean average precision (mAP) from 88.3% to 90.1% and increases Frames per second (FPS) from 20 to 52 frames. This improvement significantly reduces false positives and false negatives in defect detection. Consequently, the proposed algorithm enhances detection speed and improves inspection efficiency, providing a viable solution for real-time detection and deployment at the edge of power distribution networks.

Targeted Training for Multi-organization Recommendation

Making recommendations for users in diverse organizations ( orgs ) is a challenging task for workplace social platforms such as Microsoft Teams and Slack. The current industry-standard model training approaches either use data from all organizations to maximize information or train organization-specific models to minimize noise. Our real-world experiments show that both approaches are poorly suited for the multi-org recommendation setting where different organizations’ interaction patterns vary in their generalizability. We introduce targeted training , which improves on standard practices by automatically selecting a subset of orgs for model development whose data are cleanest and best represent global trends. We demonstrate how and when targeted training improves over global training through theoretical analysis and simulation. Our experiments on large-scale datasets from Microsoft Teams, SharePoint, Stack Exchange, DBLP, and Reddit show that in many cases targeted training can improve mean average precision (MAP) across orgs by 10–15% over global training, is more robust to orgs with lower data quality, and generalizes better to unseen orgs. Our training framework is applicable to a wide range of inductive recommendation models, from simple regression models to graph neural networks (GNNs).

Research on the identification and detection of field pests in the complex background based on the rotation detection algorithm.

As a large agricultural and population country, China's annual demand for food is significant. The crop yield will be affected by various natural disasters every year, and one of the most important factors affecting crops is the impact of insect pests. The key to solving the problem is to detect, identify and provide feedback in time at the initial stage of the pest. In this paper, according to the pest picture data obtained through the pest detection lamp in the complex natural background and the marking categories of agricultural experts, the pest data set pest rotation detection (PRD21) in different natural environments is constructed. A comparative study of image recognition is carried out through different target detection algorithms. The final experiment proves that the best algorithm for rotation detection improves mean Average Precision by 18.5% compared to the best algorithm for horizontal detection, reaching 78.5%. Regarding Recall, the best rotation detection algorithm runs 94.7%, which is 7.4% higher than horizontal detection. In terms of detection speed, the rotation detection time of a picture is only 0.163s, and the model size is 66.54MB, which can be embedded in mobile devices for fast detection. This experiment proves that rotation detection has a good effect on pests' detection and recognition rate, which can bring new application value and ideas, provide new methods for plant protection, and improve grain yield.

Road defect detection from on-board cameras with scarce and cross-domain data

Deep learning methods have attained promising performance on road defect detection from on-board cameras. However, they oftentimes rely heavily on well-annotated datasets with sufficient samples, limiting the practical applications when only few labeled samples are available. To fill this gap, this paper proposes a framework based on Faster Region-Convolutional Neural Network (Faster R-CNN) for road defect detection with scarce and cross-domain data. First, a defect weighting branch is developed to enable Faster R-CNN to quickly learn to detect road defects with few annotated data, then a data augmentation method is proposed to enlarge the abundance of annotated data and alleviate the cross-domain issue. Experimental results demonstrate that the proposed framework has attained better performance compared to a state-of-the-art few-shot detector, in terms of an improved mean average precision of 1.83% when only limited samples (i.e., 30 images per category) are provided for training. In the future, the proposed framework could also be extended to other detection tasks with limited data (e.g., construction vehicle detection), allowing humans to reduce their efforts and time required for arduous data collection and annotation.

Deep Learning-Based Defect Detection From Sequences of Ultrasonic B-Scans

Ultrasonic testing (UT) is one of the commonly used non-destructive testing (NDT) techniques for material evaluation and defect detection. The acquisition of UT data is largely performed automatically by using various robotic manipulators which can ensure the consistency of the recorded data. On the other hand, complete analysis of the acquired data is still performed manually by trained personnel. This makes the reliability of defect detection highly dependent on humans’ knowledge and experience. Most of the previous attempts for automated defect detection from UT data analyze individual A-scans. In such cases, valuable information present in the surrounding A-scans remains unused and limits the performance of such methods. The situation is better if a B-scan is used as an input, especially if the dataset is large enough to train a deep learning object detector. However, if each of the B-scans is analyzed individually, as it was done so far in the literature, there is still valuable information left in the surrounding B-scans that could be used to improve the precision. We showed that expanding the input layer of an existing method will not lead to an improvement and that a more complex approach is needed in order to effectively use information from neighboring B-scans. We propose two approaches based on high-dimensional feature maps merging. We showed that proposed models improve mean average precision (mAP) compared to the previous state-of-the-art model by 2% for input resolutions of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$512\times 512$ </tex-math></inline-formula> pixels, and 3.4% for input resolutions of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$384\times 384$ </tex-math></inline-formula> pixels.

Generative Adversarial Networks for Data Augmentation in Structural Adhesive Inspection

The technological advances brought forth by the Industry 4.0 paradigm have renewed the disruptive potential of artificial intelligence in the manufacturing sector, building the data-driven era on top of concepts such as Cyber–Physical Systems and the Internet of Things. However, data availability remains a major challenge for the success of these solutions, particularly concerning those based on deep learning approaches. Specifically in the quality inspection of structural adhesive applications, found commonly in the automotive domain, defect data with sufficient variety, volume and quality is generally costly, time-consuming and inefficient to obtain, jeopardizing the viability of such approaches due to data scarcity. To mitigate this, we propose a novel approach to generate synthetic training data for this application, leveraging recent breakthroughs in training generative adversarial networks with limited data to improve the performance of automated inspection methods based on deep learning, especially for imbalanced datasets. Preliminary results in a real automotive pilot cell show promise in this direction, with the approach being able to generate realistic adhesive bead images and consequently object detection models showing improved mean average precision at different thresholds when trained on the augmented dataset. For reproducibility purposes, the model weights, configurations and data encompassed in this study are made publicly available.

Simple Fusion of Object Detectors for Improved Performance and Faster Deployment

Object detectors often suffer from multiple performance limitations which may be attenuated with larger training datasets, improved training techniques, and complex detection models. However, such strategies are complex and time-consuming for applications requiring fast deployments. We propose a Simple Fusion of Object Detectors (SFOD) late ensemble method to combine existing pre-trained, off-the-shelf, fine-tuned object detectors and leverage on their divergences to improve the overall detection performance. Comprehensive experimental evaluations, based on PASCAL VOC07 challenge, demonstrate SFOD’s ability to improve mean average precision ( ${mAP}$ ) for different fusion sizes and base detector combinations, reaching an absolute 84.08% ${mAP}$ and an improvement of 3.97% ${mAP}$ . The improvements extend to most classes, fusion sizes, and base detector combinations, revealing $AP$ improvements up to 17.35% over baselines, for particular object classes. Practical application evaluations, based on optimal threshold selection, also reveal improvements of 10.54% and 8.36% of mean recall ( $mR$ ) and ${mAP}$ , respectively. Our approach does not require additional training and is quickly deployable, yet providing a few adjustable hyperparameters to optimize the recall-precision relation for specific applications. Improvements obtained from our proposed SFOD fusion pipeline span across a broad range of object classes and are important for a wide variety of critical applications where every successful detection is treasured.

TCM: Temporal Consistency Model for Head Detection in Complex Videos

Head detection in real-world videos is a classical research problem in computer vision. Head detection in videos is challenging than in a single image due to many nuisances that are commonly observed in natural videos, including arbitrary poses, appearances, and scales. Generally, head detection is treated as a particular case of object detection in a single image. However, the performance of object detectors deteriorates in unconstrained videos. In this paper, we propose a temporal consistency model (TCM) to enhance the performance of a generic object detector by integrating spatial-temporal information that exists among subsequent frames of a particular video. Generally, our model takes detection from a generic detector as input and improves mean average precision (mAP) by recovering missed detection and suppressing false positives. We compare and evaluate the proposed framework on four challenging datasets, i.e., HollywoodHeads, Casablanca, BOSS, and PAMELA. Experimental evaluation shows that the performance is improved by employing the proposed TCM model. We demonstrate both qualitatively and quantitatively that our proposed framework obtains significant improvements over other methods.

Application Research of Fast UAV Aerial Photography Object Detection and Recognition Based on Improved YOLOv3

UAV aerial photography is affected by weather, altitude, illumination, occlusion and other factors resulting in the objective fact that the object has many variations in scale and perspective. This paper proposes an improved YOLOv3 algorithm which achieve object detection and recognition in view of the above complex working conditions quickly and accurately. Firstly, the network framework of YOLOv3 is altered and the BN layer is integrated into the convolution layer. The network structure is simplified and the speed of model detection is greatly accelerated but the detection accuracy is almost the same as original. Secondly, GIOU is adopted as the loss function for bounding box regression to prevent the existence of zero gradient and improve mean average precision (mAP) of the detection model. Finally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes independently. The results of a large number of comparative experiments show that the improved YOLOv3 algorithm can achieve object detection for the UAV aerial photography faster and more accurately. The detection speed is increased by 27%, the mAP is increased by 2.32% and the detection performance is also improved for small object significantly compared with YOLOv3.

Robust image retrieval by cascading a deep quality assessment network

The performance of computer vision algorithms can severely degrade in the presence of a variety of distortions. While image enhancement algorithms have evolved to optimize image quality as measured according to human visual perception, their relevance in maximizing the success of computer vision algorithms operating on the enhanced image has been much less investigated. We consider the problem of image enhancement to combat Gaussian noise and low resolution with respect to the specific application of image retrieval from a dataset. We define the notion of image quality as determined by the success of image retrieval and design a deep convolutional neural network (CNN) to predict this quality. This network is then cascaded with a deep CNN designed for image denoising or super resolution, allowing for optimization of the enhancement CNN to maximize retrieval performance. This framework allows us to couple enhancement to the retrieval problem. We also consider the problem of adapting image features for robust retrieval performance in the presence of distortions. We show through experiments on distorted images of the Oxford and Paris buildings datasets that our algorithms yield improved mean average precision when compared to using enhancement methods that are oblivious to the task of image retrieval. 11The material in this paper appeared in part at the 2018 IEEE International Conference on Image Processing, Athens, Greece.

Region-Based Convolutional Networks for Accurate Object Detection and Segmentation.

Object detection performance, as measured on the canonical PASCAL VOC Challenge datasets, plateaued in the final years of the competition. The best-performing methods were complex ensemble systems that typically combined multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 50 percent relative to the previous best result on VOC 2012-achieving a mAP of 62.4 percent. Our approach combines two ideas: (1) one can apply high-capacity convolutional networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data are scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, boosts performance significantly. Since we combine region proposals with CNNs, we call the resulting model an R-CNN or Region-based Convolutional Network. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn.

Indian topographical map symbols understanding system

Symbol understanding is a pre-requisite for reading and interpretation of any map. Thus, any topographic map-based automated system needs to understand generic symbols associated with the set of maps. The objective of this study was to develop an automated system for the understanding of Survey of India topographic map symbols. The system has been developed making use of shape analysis adopting a complex-valued chain coding method for representation of the (exterior) boundary of the symbol. Fourier discrete transform and autocorrelation function have been used for shape descriptions. Classification and recognition have been implemented through a template matching method based on similarity measures. The system has been trained with 150 samples of 20 Indian topographic symbols and tested for 200 samples of each of the 20 symbols extracted from sample maps. Experimental results showed that the proposed method has an overall recognition rate of 84.68% as well as the improved mean average precision of symbol recognition. However, understanding of interconnected and/or crowded symbols may be taken up as future scope of this work.

Adapting information retrieval systems to user queries

Users enter queries that are short as well as long. The aim of this work is to evaluate techniques that can enable information retrieval (IR) systems to automatically adapt to perform better on such queries. By adaptation we refer to (1) modifications to the queries via user interaction, and (2) detecting that the original query is not a good candidate for modification. We show that the former has the potential to improve mean average precision (MAP) of long and short queries by 40% and 30% respectively, and that simple user interaction can help towards this goal. We observed that after inspecting the options presented to them, users frequently did not select any. We present techniques in this paper to determine beforehand the utility of user interaction to avoid this waste of time and effort. We show that our techniques can provide IR systems with the ability to detect and avoid interaction for unpromising queries without a significant drop in overall performance.

Paper Filtering Method Using Features of Co-Author Research Group, Subject Category and Terminology

A paper filtering system that supports the effective collection of related technical papers is becoming important as the technological progress has been rapid. Two requirements for the paper filtering system are [A] the reduction of user's workload in specifying filtering conditions and [B] sufficient filtering accuracy. In this paper, we propose a paper filtering method that meets both [A] and [B] simultaneously by focusing on feartures of co-author research group, subject category, and terminology. The result of evaluation using 3,600 domestic society papers shows that the proposed methods improved mean average precision from 0.39 to 0.50 by 0.11 comparing with conventional pseudo relevance feedback method, which becomes closer to practical use.