Vehicle-Related Distance Estimation Using Customized YOLOv7

Liu, Xiaoxu; Yan, Wei Qi

doi:10.1007/978-3-031-25825-1_7

Cited by 10 publications

(5 citation statements)

References 46 publications

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“…According to a study, YOLOv7 has higher inference in speed and accuracy when compared with other algorithms such as YOLOR, PP-YOLOE, YOLOX, Scaled-YOLOv4, and YOLOv5 (r6.1) 57 . In several recent research, the detection accuracy and precision of the YOLOv7 algorithm have also been evaluated and reported 47,56,[58][59][60] .…”

Section: Visualization and Discussionmentioning

confidence: 99%

Tea leaf disease detection and identification based on YOLOv7 (YOLO-T)

Soeb

Jubayer

Tarin

et al. 2023

Sci Rep

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A reliable and accurate diagnosis and identification system is required to prevent and manage tea leaf diseases. Tea leaf diseases are detected manually, increasing time and affecting yield quality and productivity. This study aims to present an artificial intelligence-based solution to the problem of tea leaf disease detection by training the fastest single-stage object detection model, YOLOv7, on the diseased tea leaf dataset collected from four prominent tea gardens in Bangladesh. 4000 digital images of five types of leaf diseases are collected from these tea gardens, generating a manually annotated, data-augmented leaf disease image dataset. This study incorporates data augmentation approaches to solve the issue of insufficient sample sizes. The detection and identification results for the YOLOv7 approach are validated by prominent statistical metrics like detection accuracy, precision, recall, mAP value, and F1-score, which resulted in 97.3%, 96.7%, 96.4%, 98.2%, and 0.965, respectively. Experimental results demonstrate that YOLOv7 for tea leaf diseases in natural scene images is superior to existing target detection and identification networks, including CNN, Deep CNN, DNN, AX-Retina Net, improved DCNN, YOLOv5, and Multi-objective image segmentation. Hence, this study is expected to minimize the workload of entomologists and aid in the rapid identification and detection of tea leaf diseases, thus minimizing economic losses.

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Section: Visualization and Discussionmentioning

confidence: 99%

Tea leaf disease detection and identification based on YOLOv7 (YOLO-T)

Soeb

Jubayer

Tarin

et al. 2023

Sci Rep

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“…It is the seventh iteration of the YOLO (You Only Look Once) series of object detection models. YOLOv7 is known for its high accuracy and speed, making it suitable for use in a variety of applications such as self-driving cars [24], surveillance systems [25], and robotics [26]. It is trained on a large dataset of images and uses a single neural network architecture to predict the class and location of objects in an image.…”

Section: Object Detectionmentioning

confidence: 99%

StereoYolo+DeepSORT: a framework to track fish from underwater stereo camera in situ

Saad,

Jakobsen,

Bondø

et al. 2024

Sixteenth International Conference on Machine Vision (ICMV 2023)

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This paper presents a 3D multiple object detection and tracking framework for identifying and quantifying changes in fish behaviour through tracking the 3D position, distance and speed of fish with respect to an underwater stereo camera. The framework consists of six essential modules based on 3D object detection to identify fish and multiple object tracking algorithms to track the fish in sequential frames. In particular, the latest version of Yolo (Yolov7) is utilised for object detection and the deep SORT algorithm is used for multiple object tracking. The framework was tested using videos captured from an underwater stereo camera in an industrial-scale sea-based fish farm. The results showed that the framework was able to accurately detect and track multiple fish in 3D. The fish position, distance and speed relative to the camera were also successfully detected. The results of this study demonstrate the effectiveness of this framework in identifying and quantifying changes in fish behaviour. The proposed novel framework has the potential to greatly enhance our understanding of fish behaviour in their natural habitats, leading to new insights into fish ecology and behaviour, while at the same time, it can enable researchers to study fish behaviour in a more detailed and accurate way.

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“…Liu et. Al [5] investigated to build a robust and fast model for estimation of vehicle distance. Synthesized theoretical insights in the context of odometry and based on proposed conceptual framework YOLOv7 is customized.…”

Section: Literature Surveymentioning

confidence: 99%

SAR Image based Target Detection & Identification using Deep Learning Model YOLOv7

2023

5th ISSE National Conference (INAC-05) on Systems Approach for Self-Reliance in Advanced Technologies

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In this paper we have implemented a Real Time ship Target Detection on SAR Imagery Using deep learning model. Ship detection is crucial to neutralize marine targets during combat scenario. SAR images operate in allweather conditions and all-day and independent of range. SAR imagery-based surveillance technology is particularly well suited for marine surveillance. An Open-source SAR-ship dataset (HRSID) along with custom dataset is used for detection and identification. The existing AI based methods of detection (two stage object detectors) have proposed regions followed by extraction of features. The speed of the process for these methods is slow for real-time applications. Hence one stage detector, YOLOv7 with 415 convolution layers is used for the application which will overcome constraints of the previous methods. Mean Average Precision (mAP) of 78% @ 0.5 IOU is achieved for test HRSID dataset and custom dataset. NVIDIA GPU (RTX A4000) hardware is used for the training. The inference time of 8.2 milliseconds is achieved on NVIDIA RTX A4000 GPU. Hence YOLOv7 is suitable for real time detection of military targets on SAR images.

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Vehicle-Related Distance Estimation Using Customized YOLOv7

Cited by 10 publications

References 46 publications

Tea leaf disease detection and identification based on YOLOv7 (YOLO-T)

Tea leaf disease detection and identification based on YOLOv7 (YOLO-T)

StereoYolo+DeepSORT: a framework to track fish from underwater stereo camera in situ

SAR Image based Target Detection & Identification using Deep Learning Model YOLOv7

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