Real time object detection for aerial search and rescue missions for missing persons

Domozi, Zsolt; Stojcsics, Dániel; Benhamida, Abdallah; Kozlovszky, Miklós; Molnár, András

doi:10.1109/sose50414.2020.9130475

Cited by 25 publications

(9 citation statements)

References 14 publications

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“…where equation ( 6) represents a generally weighted feature fusion but without constraints on wi, it can lead to convergence difficulties, which do not align with our requirements. Equation (7) represents feature fusion using Softmax. Although it achieves convergence, the use of numerous exponential operations makes it inefficient and does not meet our requirements.…”

Section: Neck and Head Improvementsmentioning

confidence: 99%

“…Firstly, the hardware equipped with UAVs is often resource-constrained, raising an urgent demand in lightweight deployed models for fast inference and low latency [5]. Secondly, as shown in Figure 1, compared to traditional images, drone aerial images have multitudes of small and complex targets, as well as complex backgrounds [6] and overlapping occlusions [7], which are caused by aerial photography angles. The complexity of images, their larger scene, and other characteristics present major challenges in target detection [8].…”

Section: Introductionmentioning

confidence: 99%

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SSE-YOLO: Efficient UAV Target Detection With Less Parameters and High Accuracy

Lu,

Sun

2024

Preprint

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Despite UAV multi-target detection exhibits considerable developmental potential worldwide, it suffers distinct challenges compared with traditional tasks in this field. These challenges include insufficient feature extraction capabilities for small targets, limited capabilities for multi-dimensional feature fusion, as well as constraints on hardware computation parameters. Especially in mission scenarios such as disaster detection, these challenges will be further amplified. Consequently, this paper introduces SSE-YOLO, an innovative YOLO framework algorithm specially designed to address these challenges. To enhance the model's feature extraction capability, we employ the SPDConv module to replace the original Conv in the backbone section, utilizing depth-separable convolution instead of traditional convolution pooling. Concurrently, we eliminate the SPPF module at the bottom and address a new Separate Kernel Attention Pyramid Pooling (SKAPP) module, substantially enhancing the feature fusion capability at the model's core. Moreover, to address the challenge of multi-dimensional feature fusion, we replace the Concat module of the neck and head with E-BiFPN, transmitting feature information from the backbone to the lower network through four CBS blocks, which effectively resolves the issue of lost contextual information in the model. Meanwhile, SSE-YOLO undergoes ablation experiments on the VisDrone2019 dataset to evaluate its effectiveness against alternative methods, and experimental results illustrate the model's exceptional precision in detecting UAV targets. In comparison to models with comparable experimental accuracy, SSE-YOLO requires remarkably fewer parameters. On the VisDrone2019-DET-test-dev dataset, SSE-YOLO enhances mAP by 17.3%, with a 42.5% reduction in the parameter amounts. Therefore, the proposed method effectively tackles the challenge of reconciling low parameters and high accuracy, presenting a novel pathway for deep learning-based UAV multi-target detection.

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Section: Neck and Head Improvementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SSE-YOLO: Efficient UAV Target Detection With Less Parameters and High Accuracy

Lu,

Sun

2024

Preprint

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“…Soltani et al [52] have developed a Tiny Deep CNN model for Signal Modulation Classification that identifies signals SNR region for wireless networks. A Tiny AI model on Unmanned Aerial Vehicles (UAV) has been proposed by Domozi et al [53] to detect objects in search and rescue missions.…”

Section: Tiny Ai and Edge: Research And Frameworkmentioning

confidence: 99%

“…It aims to provide distributed, low-latency, reliable, scalable, and private AI services [35]. Many applications that require real-time responses can utilize edgeAI, such as autonomous vehicles, smart homes, smart cities, and security [47][48][49][50][51][52][53]. There are some works that have considered distributed AI for healthcare, which is the focus of this work too.…”

Section: Distributed Ai In Healthcarementioning

confidence: 99%

Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge

Janbi

Mehmood

Katib

et al. 2022

Sensors

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Several factors are motivating the development of preventive, personalized, connected, virtual, and ubiquitous healthcare services. These factors include declining public health, increase in chronic diseases, an ageing population, rising healthcare costs, the need to bring intelligence near the user for privacy, security, performance, and costs reasons, as well as COVID-19. Motivated by these drivers, this paper proposes, implements, and evaluates a reference architecture called Imtidad that provides Distributed Artificial Intelligence (AI) as a Service (DAIaaS) over cloud, fog, and edge using a service catalog case study containing 22 AI skin disease diagnosis services. These services belong to four service classes that are distinguished based on software platforms (containerized gRPC, gRPC, Android, and Android Nearby) and are executed on a range of hardware platforms (Google Cloud, HP Pavilion Laptop, NVIDIA Jetson nano, Raspberry Pi Model B, Samsung Galaxy S9, and Samsung Galaxy Note 4) and four network types (Fiber, Cellular, Wi-Fi, and Bluetooth). The AI models for the diagnosis include two standard Deep Neural Networks and two Tiny AI deep models to enable their execution at the edge, trained and tested using 10,015 real-life dermatoscopic images. The services are evaluated using several benchmarks including model service value, response time, energy consumption, and network transfer time. A DL service on a local smartphone provides the best service in terms of both energy and speed, followed by a Raspberry Pi edge device and a laptop in fog. The services are designed to enable different use cases, such as patient diagnosis at home or sending diagnosis requests to travelling medical professionals through a fog device or cloud. This is the pioneering work that provides a reference architecture and such a detailed implementation and treatment of DAIaaS services, and is also expected to have an extensive impact on developing smart distributed service infrastructures for healthcare and other sectors.

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“…4 For object detection, several deep learning methods have been proposed, e.g. YOLO, 4 R-CNN, 5 and SSD, 6 with promising results for autonomous driving, 7 robotic perception, 8 search and rescue, 9 surveillance, 10 and military object detection. 11 However, detection of small object remains an open challenge.…”

Section: Introductionmentioning

confidence: 99%

Leveraging temporal context in deep learning methodology for small object detection

Heslinga,

Ruis,

Ballan

et al. 2023

Artificial Intelligence for Security and Defence Applications

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Automated object detection is becoming more relevant in a wide variety of applications in the military domain. This includes the detection of drones, ships, and vehicles in video and IR video. In recent years, deep learningbased object detection methods, such as YOLO, have shown to be promising in many applications for object detection. However, current methods have limited success when objects of interest are small in number of pixels, e.g. objects far away or small objects closer by. This is important, since accurate small object detection translates to early detection and the earlier an object is detected the more time is available for action. In this study, we investigate novel image analysis techniques that are designed to address some of the challenges of (very) small object detection by taking into account temporal information. We implement six methods, of which three are based on deep learning and use the temporal context of a set of frames within a video. The methods consider neighboring frames when detecting objects, either by stacking them as additional channels or by considering difference maps. We compare these spatio-temporal deep learning methods with YOLO-v8 that only considers single frames and two traditional moving object detection methods. Evaluation is done on a set of videos that encompasses a wide variety of challenges, including various objects, scenes, and acquisition conditions to show real-world performance.

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Real time object detection for aerial search and rescue missions for missing persons

Cited by 25 publications

References 14 publications

SSE-YOLO: Efficient UAV Target Detection With Less Parameters and High Accuracy

SSE-YOLO: Efficient UAV Target Detection With Less Parameters and High Accuracy

Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge

Leveraging temporal context in deep learning methodology for small object detection

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