Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

Recent outstanding results of supervised object detection in competitions and challenges are often associated with specific metrics and datasets. The evaluation of such methods applied in different contexts have increased the demand for annotated datasets. Annotation tools represent the location and size of objects in distinct formats, leading to a lack of consensus on the representation. Such a scenario often complicates the comparison of object detection methods. This work alleviates this problem along the following lines: (i) It provides an overview of the most relevant evaluation methods used in object detection competitions, highlighting their peculiarities, differences, and advantages; (ii) it examines the most used annotation formats, showing how different implementations may influence the assessment results; and (iii) it provides a novel open-source toolkit supporting different annotation formats and 15 performance metrics, making it easy for researchers to evaluate the performance of their detection algorithms in most known datasets. In addition, this work proposes a new metric, also included in the toolkit, for evaluating object detection in videos that is based on the spatio-temporal overlap between the ground-truth and detected bounding boxes.

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A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit

A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges

Human Activity Recognition (HAR) employing inertial motion data has gained considerable momentum in recent years, both in research and industrial applications. From the abstract perspective, this has been driven by an acceleration in the building of intelligent and smart environments and systems that cover all aspects of human life including healthcare, sports, manufacturing, commerce, etc. Such environments and systems necessitate and subsume activity recognition, aimed at recognizing the actions, characteristics, and goals of one or more individuals from a temporal series of observations streamed from one or more sensors. Due to the reliance of conventional Machine Learning (ML) techniques on handcrafted features in the extraction process, current research suggests that deep-learning approaches are more applicable to automated feature extraction from raw sensor data. In this work, the generic HAR framework for smartphone sensor data is proposed, based on Long Short-Term Memory (LSTM) networks for time-series domains. Four baseline LSTM networks are comparatively studied to analyze the impact of using different kinds of smartphone sensor data. In addition, a hybrid LSTM network called 4-layer CNN-LSTM is proposed to improve recognition performance. The HAR method is evaluated on a public smartphone-based dataset of UCI-HAR through various combinations of sample generation processes (OW and NOW) and validation protocols (10-fold and LOSO cross validation). Moreover, Bayesian optimization techniques are used in this study since they are advantageous for tuning the hyperparameters of each LSTM network. The experimental results indicate that the proposed 4-layer CNN-LSTM network performs well in activity recognition, enhancing the average accuracy by up to 2.24% compared to prior state-of-the-art approaches.

https://www.mdpi.com/1424-8220/21/5/1636/pdf

LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes

Few-shot cotton pest recognition and terminal realization

Sensor-based and vision-based human activity recognition: A comprehensive survey

Crop pest recognition in natural scenes using convolutional neural networks

As a key technology of intelligent transportation system, the intelligent vehicle is the carrier of comprehensive integration of many technologies. Although vision-based autonomous driving has shown excellent prospects, there is still a problem of how to analyze the complicated traffic situation by the collected data. Recently, autonomous driving has been formulated as many tasks separately by using different models, such as object detection task and intention recognition task. In this study, a vision-based system was developed to detect and identity various objects and predict the intention of pedestrians in the traffic scene. The main contributions of this research are (1) an optimized model was presented to detect 10 kinds of objects based on the structure of YOLOv4; (2) a fine-tuned Part Affinity Fields approach was proposed to estimate the pose of pedestrians; (3) Explainable Artificial Intelligence (XAI) technology is added to explain and assist the estimation results in the risk assessment phase; (4) an elaborate self-driving dataset that includes several different subsets for each corresponding task was introduced; and (5) an end-to-end system containing multiple models with high accuracy was developed. Experimental results proved that the total parameters of optimized YOLOv4 are reduced by 74%, which satisfies the real-time capability. In addition, the detection precision of the optimized YOLOv4 achieved an improvement of 2.6% compared to the state-of-the-art.

/pdf/a-deep-learning-based-hybrid-framework-for-object-detection-1tl879l1bj.pdf

Hanxiang Wang

Papers

Sensor-based and vision-based human activity recognition: A comprehensive survey

Crop pest recognition in natural scenes using convolutional neural networks

A Deep Learning-Based Hybrid Framework for Object Detection and Recognition in Autonomous Driving

DefectTR: End-to-end defect detection for sewage networks using a transformer

A Robust Instance Segmentation Framework for Underground Sewer Defect Detection