A Real-Time Forward Collision Warning Technique Incorporating Detection and Depth Estimation Networks

Advanced driver assistance systems (ADASs) are becoming increasingly common in modern-day vehicles, as they not only improve safety and reduce accidents but also aid in smoother and easier driving. ADASs rely on a variety of sensors such as cameras, radars, lidars, and a combination of sensors, to perceive their surroundings and identify and track objects on the road. The key components of ADASs are object detection, recognition, and tracking algorithms that allow vehicles to identify and track other objects on the road, such as other vehicles, pedestrians, cyclists, obstacles, traffic signs, traffic lights, etc. This information is then used to warn the driver of potential hazards or used by the ADAS itself to take corrective actions to avoid an accident. This paper provides a review of prominent state-of-the-art object detection, recognition, and tracking algorithms used in different functionalities of ADASs. The paper begins by introducing the history and fundamentals of ADASs followed by reviewing recent trends in various ADAS algorithms and their functionalities, along with the datasets employed. The paper concludes by discussing the future of object detection, recognition, and tracking algorithms for ADASs. The paper also discusses the need for more research on object detection, recognition, and tracking in challenging environments, such as those with low visibility or high traffic density.

“…Ref. [ 145 ] introduces a real-time FCW technique involving detection and depth estimation networks to identify nearby vehicles and estimate distances. Ref.…”

Section: Discussion—methodologymentioning

confidence: 99%

Section: Discussion—methodologymentioning

confidence: 99%

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Malligere Shivanna,

Guo

2023

“…Even further, it is possible to optimize the depth estimation by focusing solely on key objects through object detection techniques [24,[26][27][28]. This strategy maximizes computational efficiency and optimizes processing time by prioritizing the foreground of the scene, making it an attractive option for real-time applications such as autonomous driving [40,41], robotics [42], surveillance [43], or assisted surgery [44].…”

Section: Related Workmentioning

confidence: 99%

Application of Machine Vision Techniques in Low-Cost Devices to Improve Efficiency in Precision Farming

Jaramillo-Hernández,

Julian,

Marco-Detchart

et al. 2024

In the context of recent technological advancements driven by distributed work and open-source resources, computer vision stands out as an innovative force, transforming how machines interact with and comprehend the visual world around us. This work conceives, designs, implements, and operates a computer vision and artificial intelligence method for object detection with integrated depth estimation. With applications ranging from autonomous fruit-harvesting systems to phenotyping tasks, the proposed Depth Object Detector (DOD) is trained and evaluated using the Microsoft Common Objects in Context dataset and the MinneApple dataset for object and fruit detection, respectively. The DOD is benchmarked against current state-of-the-art models. The results demonstrate the proposed method’s efficiency for operation on embedded systems, with a favorable balance between accuracy and speed, making it well suited for real-time applications on edge devices in the context of the Internet of things.

“…Felzenszwalb et al [ 4 ] presented a pedestrian detection approach that used a deformable part model with a histogram of oriented gradients and a support vector machine. Recently, the advent of convolutional neural networks (CNNs) [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ] rapidly superseded traditional object detection. These deep neural networks hypothesize bounding boxes, extract features from them, and use high-quality object classifiers.…”

Section: Introductionmentioning

confidence: 99%

Object Detection and Depth Estimation Approach Based on Deep Convolutional Neural Networks

Wang

Lin

Chang

2021

Self Cite

In this paper, we present a real-time object detection and depth estimation approach based on deep convolutional neural networks (CNNs). We improve object detection through the incorporation of transfer connection blocks (TCBs), in particular, to detect small objects in real time. For depth estimation, we introduce binocular vision to the monocular-based disparity estimation network, and the epipolar constraint is used to improve prediction accuracy. Finally, we integrate the two-dimensional (2D) location of the detected object with the depth information to achieve real-time detection and depth estimation. The results demonstrate that the proposed approach achieves better results compared to conventional methods.