Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images

A few lightweight convolutional neural network (CNN) models have been recently designed for remote sensing object detection (RSOD). However, most of them simply replace vanilla convolutions with stacked separable convolutions, which may not be efficient due to a lot of precision losses and may not be able to detect oriented bounding boxes (OBB). Also, the existing OBB detection methods are difficult to constrain the shape of objects predicted by CNNs accurately. In this paper, we propose an effective lightweight oriented object detector (LO-Det). Specifically, a channel separation-aggregation (CSA) structure is designed to simplify the complexity of stacked separable convolutions, and a dynamic receptive field (DRF) mechanism is developed to maintain high accuracy by customizing the convolution kernel and its perception range dynamically when reducing the network complexity. The CSA-DRF component optimizes efficiency while maintaining high accuracy. Then, a diagonal support constraint head (DSC-Head) component is designed to detect OBBs and constrain their shapes more accurately and stably. Extensive experiments on public datasets demonstrate that the proposed LO-Det can run very fast even on embedded devices with the competitive accuracy of detecting oriented objects.Comment: 15 page

YOLO-Drone:Airborne real-time detection of dense small objects from high-altitude perspective

Unmanned Aerial Vehicles (UAVs), specifically drones equipped with remote sensing object detection technology, have rapidly gained a broad spectrum of applications and emerged as one of the primary research focuses in the field of computer vision. Although UAV remote sensing systems have the ability to detect various objects, small-scale objects can be challenging to detect reliably due to factors such as object size, image degradation, and real-time limitations. To tackle these issues, a real-time object detection algorithm (YOLO-Drone) is proposed and applied to two new UAV platforms as well as a specific light source (silicon-based golden LED). YOLO-Drone presents several novelties: 1) including a new backbone Darknet59; 2) a new complex feature aggregation module MSPP-FPN that incorporated one spatial pyramid pooling and three atrous spatial pyramid pooling modules; 3) and the use of Generalized Intersection over Union (GIoU) as the loss function. To evaluate performance, two benchmark datasets, UAVDT and VisDrone, along with one homemade dataset acquired at night under silicon-based golden LEDs, are utilized. The experimental results show that, in both UAVDT and VisDrone, the proposed YOLO-Drone outperforms state-of-the-art (SOTA) object detection methods by improving the mAP of 10.13% and 8.59%, respectively. With regards to UAVDT, the YOLO-Drone exhibits both high real-time inference speed of 53 FPS and a maximum mAP of 34.04%. Notably, YOLO-Drone achieves high performance under the silicon-based golden LEDs, with a mAP of up to 87.71%, surpassing the performance of YOLO series under ordinary light sources. To conclude, the proposed YOLO-Drone is a highly effective solution for object detection in UAV applications, particularly for night detection tasks where silicon-based golden light LED technology exhibits significant superiority

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Automatic CNN channel selection and effective detection on face and rotated aerial objects

Balancing accuracy and computational cost is a challenging task in computer vision. This is especially true for convolutional neural networks (CNNs), which required far larger scale of processing power than traditional learning algorithms. This thesis is aimed at the development of new CNN structures and loss functions to tackle the unbalanced accuracy-effciency issue in image classification and object detection, which are two fundamental yet challenging tasks of computer vision. For a CNN based object detector, the main computational cost is caused by the feature extractor (backbone), which has been originally applied to image classification.;Optimising the structure of CNN applied to image classification will bring benefits when it is applied to object detection. Although the outputs of detectors may vary across detection tasks, the challenges and the design principles among detectors are similar. Therefore, this thesis will start with face detection (i.e. a single object detection task), which is a significant branch of objection detection and has been widely used in real life. After that, object detection on aerial image will be investigated, which is a more challenging detection task.;Specifically, the objectives of this thesis are: 1. Optimising the CNN structures for image classification; 2. Developing a face detector which enables a trade-off between computational cost and accuracy; and 3. Proposing an object detector for aerial images, which suppresses the background noise without damaging the inference efficiency.;For the first target, this thesis aims to automatically optimise the topology of CNNs to generate the structure of fixed-length models, in which unnecessary convolutional kernels are removed. Experimental results have demonstrated that the optimised model can achieve comparable accuracy to the state-of-the-art models, across a broad range of datasets, whilst significantly reducing the number of parameters.;To tackle the unbalanced accuracy-effciency challenge in face detection, a novel context enhanced approach is proposed which improves the performance of the face detector in terms of both loss function and structure. For loss function optimisation, a hierarchical loss, referred to as 'triple loss' in this thesis, is introduced to optimise the feature pyramid network (FPN) based face detector. For structural optimisation, this thesis proposes a context-sensitive structure to increase the capacity of the network prediction. Experimental results indicate that the proposed method achieves a good balance between the accuracy and computational cost of face detection.;To suppress the background noise in aerial image object detection, this thesis presents a two-stage detector, named as 'SAFDet'. To be more specific, a rotation anchor-free-branch (RAFB) is proposed to regress the precise rectangle boundary. Asthe RAFB is anchor free, the computational cost is negligible during training. Meanwhile,a centre prediction module (CPM) is introduced to enhance the capabilities oftarget localisation and noise suppression from the background. As the CPM is only deployed during training, it does not increase the computational cost of inference. Experimental results indicate that the proposed method achieves a good balance between the accuracy and computational cost, and it effectively suppresses the background noise at the same time.Balancing accuracy and computational cost is a challenging task in computer vision. This is especially true for convolutional neural networks (CNNs), which required far larger scale of processing power than traditional learning algorithms. This thesis is aimed at the development of new CNN structures and loss functions to tackle the unbalanced accuracy-effciency issue in image classification and object detection, which are two fundamental yet challenging tasks of computer vision. For a CNN based object detector, the main computational cost is caused by the feature extractor (backbone), which has been originally applied to image classification.;Optimising the structure of CNN applied to image classification will bring benefits when it is applied to object detection. Although the outputs of detectors may vary across detection tasks, the challenges and the design principles among detectors are similar. Therefore, this thesis will start with face detection (i.e. a single object detection task), which is a significant branch of objection detection and has been widely used in real life. After that, object detection on aerial image will be investigated, which is a more challenging detection task.;Specifically, the objectives of this thesis are: 1. Optimising the CNN structures for image classification; 2. Developing a face detector which enables a trade-off between computational cost and accuracy; and 3. Proposing an object detector for aerial images, which suppresses the background noise without damaging the inference efficiency.;For the first target, this thesis aims to automatically optimise the topology of CNNs to generate the structure of fixed-length models, in which unnecessary convolutional kernels are removed. Experimental results have demonstrated that the optimised model can achieve comparable accuracy to the state-of-the-art models, across a broad range of datasets, whilst significantly reducing the number of parameters.;To tackle the unbalanced accuracy-effciency challenge in face detection, a novel context enhanced approach is proposed which improves the performance of the face detector in terms of both loss function and structure. For loss function optimisation, a hierarchical loss, referred to as 'triple loss' in this thesis, is introduced to optimise the feature pyramid network (FPN) based face detector. For structural optimisation, this thesis proposes a context-sensitive structure to increase the capacity of the network prediction. Experimental results indicate that the proposed method achieves a good balance between the accuracy and computational cost of face detection.;To suppress the background noise in aerial image object detection, this thesis presents a two-stage detector, named as 'SAFDet'. To be more specific, a rotation anchor-free-branch (RAFB) is proposed to regress the precise rectangle boundary. Asthe RAFB is anchor free, the computational cost is negligible during training. Meanwhile,a centre prediction module (CPM) is introduced to enhance the capabilities oftarget localisation and noise suppression from the background. As the CPM is only deployed during training, it does not increase the computational cost of inference. Experimental results indicate that the proposed method achieves a good balance between the accuracy and computational cost, and it effectively suppresses the background noise at the same time

Deep Learning based Vehicle Detection in Aerial Imagery

This book proposes a novel deep learning based detection method, focusing on vehicle detection in aerial imagery recorded in top view. The base detection framework is extended by two novel components to improve the detection accuracy by enhancing the contextual and semantical content of the employed feature representation. To reduce the inference time, a lightweight CNN architecture is proposed as base architecture and a novel module that restricts the search area is introduced

Deep Learning based Vehicle Detection in Aerial Imagery

Der Einsatz von luftgestützten Plattformen, die mit bildgebender Sensorik ausgestattet sind, ist ein wesentlicher Bestandteil von vielen Anwendungen im Bereich der zivilen Sicherheit. Bekannte Anwendungsgebiete umfassen unter anderem die Entdeckung verbotener oder krimineller Aktivitäten, Verkehrsüberwachung, Suche und Rettung, Katastrophenhilfe und Umweltüberwachung. Aufgrund der großen Menge zu verarbeitender Daten und der daraus resultierenden kognitiven Überbelastung ist jedoch eine Analyse der Luftbilddaten ausschließlich durch menschliche Auswerter in der Praxis nicht anwendbar. Zur Unterstützung der menschlichen Auswerter kommen daher in der Regel automatische Bild- und Videoverarbeitungsalgorithmen zum Einsatz. Eine zentrale Aufgabe bildet dabei eine zuverlässige Detektion relevanter Objekte im Sichtfeld der Kamera, bevor eine Interpretation der gegebenen Szene stattfinden kann. Die geringe Bodenauflösung aufgrund der großen Distanz zwischen Kamera und Erde macht die Objektdetektion in Luftbilddaten zu einer herausfordernden Aufgabe, welche durch Bewegungsunschärfe, Verdeckungen und Schattenwurf zusätzlich erschwert wird. Obwohl in der Literatur eine Vielzahl konventioneller Ansätze zur Detektion von Objekten in Luftbilddaten existiert, ist die Detektionsgenauigkeit durch die Repräsentationsfähigkeit der verwendeten manuell entworfenen Merkmale beschränkt. Im Rahmen dieser Arbeit wird ein neuer Deep-Learning basierter Ansatz zur Detektion von Objekten in Luftbilddaten präsentiert. Der Fokus der Arbeit liegt dabei auf der Detektion von Fahrzeugen in Luftbilddaten, die senkrecht von oben aufgenommen wurden. Grundlage des entwickelten Ansatzes bildet der Faster R-CNN Detektor, der im Vergleich zu anderen Deep-Learning basierten Detektionsverfahren eine höhere Detektionsgenauigkeit besitzt. Da Faster R-CNN wie auch die anderen Deep-Learning basierten Detektionsverfahren auf Benchmark Datensätzen optimiert wurden, werden in einem ersten Schritt notwendige Anpassungen an die Eigenschaften der Luftbilddaten, wie die geringen Abmessungen der zu detektierenden Fahrzeuge, systematisch untersucht und daraus resultierende Probleme identifiziert. Im Hinblick auf reale Anwendungen sind hier vor allem die hohe Anzahl fehlerhafter Detektionen durch fahrzeugähnliche Strukturen und die deutlich erhöhte Laufzeit problematisch. Zur Reduktion der fehlerhaften Detektionen werden zwei neue Ansätze vorgeschlagen. Beide Ansätze verfolgen dabei das Ziel, die verwendete Merkmalsrepräsentation durch zusätzliche Kontextinformationen zu verbessern. Der erste Ansatz verfeinert die räumlichen Kontextinformationen durch eine Kombination der Merkmale von frühen und tiefen Schichten der zugrundeliegenden CNN Architektur, so dass feine und grobe Strukturen besser repräsentiert werden. Der zweite Ansatz macht Gebrauch von semantischer Segmentierung um den semantischen Informationsgehalt zu erhöhen. Hierzu werden zwei verschiedene Varianten zur Integration der semantischen Segmentierung in das Detektionsverfahren realisiert: zum einen die Verwendung der semantischen Segmentierungsergebnisse zur Filterung von unwahrscheinlichen Detektionen und zum anderen explizit durch Verschmelzung der CNN Architekturen zur Detektion und Segmentierung. Sowohl durch die Verfeinerung der räumlichen Kontextinformationen als auch durch die Integration der semantischen Kontextinformationen wird die Anzahl der fehlerhaften Detektionen deutlich reduziert und somit die Detektionsgenauigkeit erhöht. Insbesondere der starke Rückgang von fehlerhaften Detektionen in unwahrscheinlichen Bildregionen, wie zum Beispiel auf Gebäuden, zeigt die erhöhte Robustheit der gelernten Merkmalsrepräsentationen. Zur Reduktion der Laufzeit werden im Rahmen der Arbeit zwei alternative Strategien verfolgt. Die erste Strategie ist das Ersetzen der zur Merkmalsextraktion standardmäßig verwendeten CNN Architektur mit einer laufzeitoptimierten CNN Architektur unter Berücksichtigung der Eigenschaften der Luftbilddaten, während die zweite Strategie ein neues Modul zur Reduktion des Suchraumes umfasst. Mit Hilfe der vorgeschlagenen Strategien wird die Gesamtlaufzeit sowie die Laufzeit für jede Komponente des Detektionsverfahrens deutlich reduziert. Durch Kombination der vorgeschlagenen Ansätze kann sowohl die Detektionsgenauigkeit als auch die Laufzeit im Vergleich zur Faster R-CNN Baseline signifikant verbessert werden. Repräsentative Ansätze zur Fahrzeugdetektion in Luftbilddaten aus der Literatur werden quantitativ und qualitativ auf verschiedenen Datensätzen übertroffen. Des Weiteren wird die Generalisierbarkeit des entworfenen Ansatzes auf ungesehenen Bildern von weiteren Luftbilddatensätzen mit abweichenden Eigenschaften demonstriert

Advances in Object and Activity Detection in Remote Sensing Imagery

The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms