Оглядово-пошукова інфрачервона система з пасивним далекоміром

Пояснювальна записка до дипломного проєкту «Оглядово-пошукова інфрачервона система з пасивним далекоміром» Обсяг роботи – 58 с., 23 ілюстрації, 2 таблиці, 19 джерел посилань. Актуальність роботи полягає в тому, що оглядово-пошукова інфрачервона система з пасивним далекоміром є важливим інструментом для вимірювання відстаней та пошуку об'єктів у різних областях. Ця система використовує теплове випромінювання об'єктів і здатна працювати в умовах обмеженої видимості, погано освітленого середовища або нічний час. Це має велике значення для безпеки, військових застосувань, пошуку та рятувальних операцій, моніторингу довкілля та багатьох інших областей. Розробка та вдосконалення таких систем допомагає покращувати точність, надійність та функціональність вимірювань та пошуку в умовах, коли інші методи можуть бути недостатньо ефективними. Об’єктом дослідження є дистанційне спостереження об’єктів в інфрачервоному діапазоні та вимірювання дальності до них. Предметом дослідження є конструктивні рішення системи для дистанційного спостереження за об’єктами, їх виявлення та розпізнавання. Мета роботи Проаналізувати методи пасивного вимірювання дальності, які можуть бути застосовані у оптичній системі дрона морського базування. Виявити недоліки та переваги над системою активного вимірювання дальності із застосуванням лазера. Розробити схемотехнічне рішення оптико-електронної системи спостереження та обгрунтувати основні конструктивні рішення.Explanatory note to the diploma project "Survey and search infrared system with passive rangefinder" The volume of the work is 58 pages, 23 illustrations, 2 tables, 19 sources of references. The relevance of the work lies in the fact that the survey and search infrared system with a passive rangefinder is an important tool for measuring distances and searching for objects in various fields. This system utilizes the thermal radiation of objects and is able to operate in low visibility, poorly lit environments, or at night. This is of great importance for security, military applications, search and rescue operations, environmental monitoring, and many other areas. The development and improvement of such systems helps to improve the accuracy, reliability, and functionality of measurements and search in conditions where other methods may not be effective enough. The object of the study is the remote observation of objects in the infrared range and measurement of the distance to them. The subject of the study is the design solutions of the system for remote monitoring of objects, their detection and recognition. The objective of the study is to analyze the methods of passive range measurement that can be used in the optical system of a sea-based drone. Identify the disadvantages and advantages over the active range measurement system using a laser. To develop a circuitry solution for an optoelectronic surveillance system and justify the main design solutions

Small Infrared Target Detection by Region-Adaptive Clutter Rejection for Sea-Based Infrared Search and Track

This paper presents a region-adaptive clutter rejection method for small target detection in sea-based infrared search and track. In the real world, clutter normally generates many false detections that impede the deployment of such detection systems. Incoming targets (missiles, boats, etc.) can be located in the sky, horizon and sea regions, which have different types of clutters, such as clouds, a horizontal line and sea-glint. The characteristics of regional clutter were analyzed after the geometrical analysis-based region segmentation. The false detections caused by cloud clutter were removed by the spatial attribute-based classification. Those by the horizontal line were removed using the heterogeneous background removal filter. False alarms by sun-glint were rejected using the temporal consistency filter, which is the most difficult part. The experimental results of the various cluttered background sequences show that the proposed region adaptive clutter rejection method produces fewer false alarms than that of the mean subtraction filter (MSF) with an acceptable degradation detection rate

Small Infrared Target Detection by Region-Adaptive Clutter Rejection for Sea-Based Infrared Search and Track

This paper presents a region-adaptive clutter rejection method for small target detection in sea-based infrared search and track. In the real world, clutter normally generates many false detections that impede the deployment of such detection systems. Incoming targets (missiles, boats, etc.) can be located in the sky, horizon and sea regions, which have different types of clutters, such as clouds, a horizontal line and sea-glint. The characteristics of regional clutter were analyzed after the geometrical analysis-based region segmentation. The false detections caused by cloud clutter were removed by the spatial attribute-based classification. Those by the horizontal line were removed using the heterogeneous background removal filter. False alarms by sun-glint were rejected using the temporal consistency filter, which is the most difficult part. The experimental results of the various cluttered background sequences show that the proposed region adaptive clutter rejection method produces fewer false alarms than that of the mean subtraction filter (MSF) with an acceptable degradation detection rate

Modelling false positive reduction in maritime object detection

Target detection has become a very significant research area in computer vision with its applications in military, maritime surveillance, and defense and security. Maritime target detection during critical sea conditions produces a number of false positives when using the existing algorithms due to sea waves, dynamic nature of the ocean, camera motion, sea glint, sensor noise, sea spray, swell and the presence of birds. The main question that has been addressed in this research is how can object detection be improved in maritime environment by reducing false positives and promoting detection rate. Most of Previous work on object detection still fails to address the problem of false positives and false negatives due to background clutter. Most of the researchers tried to reduce false positives by applying filters but filtering degrades the quality of an image leading to more false alarms during detection. As much as radar technology has previously been the most utilized method, it still fails to detect very small objects and it may be applied in special circumstances. In trying to improve the implementation of target detection in maritime, empirical research method was proposed to answer questions about existing target detection algorithms and techniques used to reduce false positives in object detection. Visible images were retrained on a pre-trained Faster R-CNN with inception v2. The pre-trained model was retrained on five different sample data with increasing size, however for the last two samples the data was duplicated to increase size. For testing purposes 20 test images were utilized to evaluate all the models. The results of this study showed that the deep learning method used performed best in detecting maritime vessels and the increase of dataset improved detection performance and false positives were reduced. The duplication of images did not yield the best results; however, the results were promising for the first three models with increasing data

Modelling false positive reduction in maritime object detection

Target detection has become a very significant research area in computer vision with its applications in military, maritime surveillance, and defense and security. Maritime target detection during critical sea conditions produces a number of false positives when using the existing algorithms due to sea waves, dynamic nature of the ocean, camera motion, sea glint, sensor noise, sea spray, swell and the presence of birds. The main question that has been addressed in this research is how can object detection be improved in maritime environment by reducing false positives and promoting detection rate. Most of Previous work on object detection still fails to address the problem of false positives and false negatives due to background clutter. Most of the researchers tried to reduce false positives by applying filters but filtering degrades the quality of an image leading to more false alarms during detection. As much as radar technology has previously been the most utilized method, it still fails to detect very small objects and it may be applied in special circumstances. In trying to improve the implementation of target detection in maritime, empirical research method was proposed to answer questions about existing target detection algorithms and techniques used to reduce false positives in object detection. Visible images were retrained on a pre-trained Faster R-CNN with inception v2. The pre-trained model was retrained on five different sample data with increasing size, however for the last two samples the data was duplicated to increase size. For testing purposes 20 test images were utilized to evaluate all the models. The results of this study showed that the deep learning method used performed best in detecting maritime vessels and the increase of dataset improved detection performance and false positives were reduced. The duplication of images did not yield the best results; however, the results were promising for the first three models with increasing data

RIVELAZIONE E LOCALIZZAZIONE DI BERSAGLI SU SCENARIO MARITTIMO MEDIANTE UN SISTEMA IR PASSIVO MULTICAMERA

La rivelazione di bersagli in scenari marittimi e la determinazione della loro posizione è un compito che risulta cruciale in molteplici contesti e, dunque, un attuale argomento di ricerca. Le tecnologie di localizzazione e sorveglianza si sono spesso avvalse di sistemi di telerilevamento attivi (radar), i quali però, oltre ad essere costosi, possono presentare problemi di compatibilità elettromagnetica in caso di vicinanza di diverse apparecchiature elettroniche. Inoltre, l’emissione di radiazioni elettromagnetiche può avere un forte impatto negativo sia sull’ambiente sia sulla salute dell’uomo. E’ di notevole interesse lo sviluppo di tecnologie che consentano di ridurre tale inquinamento elettromagnetico. Si presenta quindi un sistema passivo, che funziona cioè senza emissione di onde elettromagnetiche, per la rivelazione e la determinazione della posizione di un bersaglio, anche ad elevata distanza. Il sistema è composto da una coppia stereoscopica di termocamere opportunamente posizionate per inquadrare la stessa scena di interesse. Le termocamere possono essere collocate a piacimento, permettendo dunque al sistema di adattarsi a molteplici utilizzi. L’impiego di questo apparato per la scoperta di bersagli a bassa quota sul livello del mare consente di superare il limite di scoperta dei sistemi radar dovuto alle riflessioni delle onde emesse sulla superficie marina. Un ulteriore vantaggio rispetto a un radar, soprattutto in ambito militare, è quello di non incrementare il livello di osservabilità dell’unità o della sede dove viene montato il sistema. Nell’elaborato viene proposto un algoritmo di rivelazione e di localizzazione automatica di bersagli in ambiente marittimo, realizzato specificamente per funzionare sul sistema sopra descritto. Selezionando un bersaglio di interesse nell’immagine fornita dalla prima termocamera, l’algoritmo implementato è in grado di sfruttare la geometria che descrive il processo di acquisizione di entrambi i dispositivi, per individuare automaticamente un’area nella seconda immagine entro la quale si troverà il bersaglio scelto. Utilizzando una tecnica di rivelazione basata sulla sottrazione del background all’interno dell’area di interesse e sulla stima della linea dell’orizzonte al fine di segmentare le aree di cielo e di mare, l’algoritmo procede con il riconoscimento del baricentro del bersaglio. Nota la posizione del bersaglio sulle due immagini, l’uso congiunto dei due punti di vista differenti permette di effettuare la triangolazione e, dunque, di ricavare posizione e distanza del bersaglio. L’algoritmo è stato validato su un dataset di immagini reali acquisite in ambiente marittimo, dando risultati promettenti anche in caso di bersagli a grande distanza dalle telecamere. Per poter operare anche una valutazione quantitativa della bontà dell’algoritmo è stato implementato un simulatore in grado di ricreare dei bersagli all’interno delle immagini reali. Il simulatore permette all’utente di definire le caratteristiche dei bersagli che, essendo note, possono essere usate in seguito come ground truth da comparare con i risultati ottenuti dall’algoritmo. In fase di validazione del simulatore, questo è stato applicato anche a sequenze video, rivelandosi un utile strumento anche per il test di algoritmi di tracking. I risultati ottenuti dimostrano che il sistema è particolarmente adatto a tutte quelle situazioni nelle quali si rendono necessarie delle valutazioni sulla presenza e sulla posizione di oggetti in ambiente marittimo. Operazioni di search and rescue (uomo a mare, soccorso profughi…), di sorveglianza (intrusioni in aree portuali, lotta alla pirateria…) e di monitoraggio di incidenti (incendi a bordo di navi, unità navali alla deriva…) sono solo alcune delle possibili applicazioni che potrebbero beneficiare dell’utilizzo del sistema e dell’algoritmo di scoperta implementato