Design and implementation of a radar and camera-based obstacle classification system using machine-learning techniques

Dhouioui, Mohamed; Frikha, Tarek

doi:10.1007/s11554-021-01117-8

Cited by 19 publications

(9 citation statements)

References 16 publications

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“…By setting the box probability threshold, the boxes with a lower probability are removed, thus achieving the screening and rejection of obstacle information. Dhouioui et al [ 10 ] proposed an embedded system based on two types of data, i.e., radar signals and camera images, to identify and classify obstacles on the road. The method extracts features from the road information data collected in the sensors to obtain the feature vectors of the obstacles.…”

Section: Related Workmentioning

confidence: 99%

“…The height measurement equation for static obstacles can be obtained by bringing Equation (10) into Equation ( 8):…”

Section: Static Obstacle Detectionmentioning

confidence: 99%

“…The target generalized obstacle information can be detected by moving the vehicle (see Figure 20e,f). The obstacle detection method based on VIDAR is used to detect the target generalized obstacle, and the distance from the vehicle to the obstacle can be determined to be 3.6 m. According to Table 4 and Equation (10), the height of the obstacle can be deduced, and the height of the obstacle obtained can be determined by arithmetic average to be 0.1 m. According to the minimum height threshold of the vehicle chassis set by the RSU, the target obstacle is judged to be an unaffected obstacle, the information is transmitted to the vehicle equipment end (see Figure 19a), and the obstacle block diagram is eliminated (see Figure 19b).…”

Section: Experiments Of Obstacle Detection Based On Rsu and Vehicle C...mentioning

confidence: 99%

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Obstacle Detection Method Based on RSU and Vehicle Camera Fusion

Ding

Zhang³

et al. 2023

Sensors

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Road obstacle detection is an important component of intelligent assisted driving technology. Existing obstacle detection methods ignore the important direction of generalized obstacle detection. This paper proposes an obstacle detection method based on the fusion of roadside units and vehicle mounted cameras and illustrates the feasibility of a combined monocular camera inertial measurement unit (IMU) and roadside unit (RSU) detection method. A generalized obstacle detection method based on vision IMU is combined with a roadside unit obstacle detection method based on a background difference method to achieve generalized obstacle classification while reducing the spatial complexity of the detection area. In the generalized obstacle recognition stage, a VIDAR (Vision-IMU based identification and ranging) -based generalized obstacle recognition method is proposed. The problem of the low accuracy of obstacle information acquisition in the driving environment where generalized obstacles exist is solved. For generalized obstacles that cannot be detected by the roadside unit, VIDAR obstacle detection is performed on the target generalized obstacles through the vehicle terminal camera, and the detection result information is transmitted to the roadside device terminal through the UDP (User Data Protocol) protocol to achieve obstacle recognition and pseudo-obstacle removal, thereby reducing the error recognition rate of generalized obstacles. In this paper, pseudo-obstacles, obstacles with a certain height less than the maximum passing height of the vehicle, and obstacles with a height greater than the maximum passing height of the vehicle are defined as generalized obstacles. Pseudo-obstacles refer to non-height objects that appear to be “patches” on the imaging interface obtained by visual sensors and obstacles with a height less than the maximum passing height of the vehicle. VIDAR is a vision-IMU-based detection and ranging method. IMU is used to obtain the distance and pose of the camera movement, and through the inverse perspective transformation, it can calculate the height of the object in the image. The VIDAR-based obstacle detection method, the roadside unit-based obstacle detection method, YOLOv5 (You Only Look Once version 5), and the method proposed in this paper were applied to outdoor comparison experiments. The results show that the accuracy of the method is improved by 2.3%, 17.4%, and 1.8%, respectively, compared with the other four methods. Compared with the roadside unit obstacle detection method, the speed of obstacle detection is improved by 1.1%. The experimental results show that the method can expand the detection range of road vehicles based on the vehicle obstacle detection method and can quickly and effectively eliminate false obstacle information on the road.

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Section: Related Workmentioning

confidence: 99%

“…The height measurement equation for static obstacles can be obtained by bringing Equation (10) into Equation ( 8):…”

Section: Static Obstacle Detectionmentioning

confidence: 99%

Section: Experiments Of Obstacle Detection Based On Rsu and Vehicle C...mentioning

confidence: 99%

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Obstacle Detection Method Based on RSU and Vehicle Camera Fusion

Ding

Zhang³

et al. 2023

Sensors

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“…e author(s) declare(s) that there are no conflicts of interest regarding the publication of this paper. Blockchain is used in different application areas such as smart cities (smart parking [12,48], water meter [49,50], etc. ).…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…e use of blockchain is not only related to cryptocurrency (Bitcoin, Ethereum, etc.) but also other sectors that are starting to work on concrete use cases include industry via Industry 4.0 & 5.0 [6], e-health [7][8][9] such as medical records tracking and EHR [7,10], paramedical and sports applications [2], and smart cities [11,12].…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Based Supply Chain System for Olive Fields Using WSNs

Ghorbel

Frikha

hajji

et al. 2022

Computational Intelligence and Neuroscience

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The agricultural domain in developing countries is mostly dictated by archaic rules based on traditions and inherited practices. With the evolution of digitalization and technology, it seems essential to apply new technologies to the agricultural field. Among the technologies to be exploited in agriculture, we mention sensors, IoT, WSN, cloud, blockchain, etc. We talk about smart agriculture in this case. In this paper, we propose a platform secured by blockchain for monitoring and securing production. This platform uses IoT connected sensors to track and save data. Our system is used to monitor the production process of olive trees. The goal is to track everything that enters and leaves our olive tree production from fertilizers, insecticides, and fortifiers to olives, trimming etc. The blockchain via its decentralized system allow a secure, irreversible, and clear monitoring. A dashboard allow us to highlight the changes while facilitating the work of farmers. Our prototype will be embedded via a Raspberry Pi 4 platform.

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