Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

Kasper-Eulaers, Margrit; Hahn, Nico; Berger, Stian; Sebulonsen, Tom; Myrland, Øystein; Kummervold, Per Egil

doi:10.3390/a14040114

Cited by 146 publications

(60 citation statements)

References 50 publications

(54 reference statements)

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“…Apart from this, YOLOv5 uses the following choices for training [ 54 ]: Activation and optimization: YOLOv5 uses leaky ReLU and sigmoid activation and SGD and ADAM as optimizer options Loss function: it uses binary cross-entropy with logits loss …”

Section: Methodsmentioning

confidence: 99%

Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50

Zhang

Sun

et al. 2021

Journal of Healthcare Engineering

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Computer-aided diagnosis (CAD) has nearly fifty years of history and has assisted many clinicians in the diagnosis. With the development of technology, recently, researches use the deep learning method to get high accuracy results in the CAD system. With CAD, the computer output can be used as a second choice for radiologists and contribute to doctors doing the final right decisions. Chest abnormality detection is a classic detection and classification problem; researchers need to classify common thoracic lung diseases and localize critical findings. For the detection problem, there are two deep learning methods: one-stage method and two-stage method. In our paper, we introduce and analyze some representative model, such as RCNN, SSD, and YOLO series. In order to better solve the problem of chest abnormality detection, we proposed a new model based on YOLOv5 and ResNet50. YOLOv5 is the latest YOLO series, which is more flexible than the one-stage detection algorithms before. The function of YOLOv5 in our paper is to localize the abnormality region. On the other hand, we use ResNet, avoiding gradient explosion problems in deep learning for classification. And we filter the result we got from YOLOv5 and ResNet. If ResNet recognizes that the image is not abnormal, the YOLOv5 detection result is discarded. The dataset is collected via VinBigData’s web-based platform, VinLab. We train our model on the dataset using Pytorch frame and use the mAP, precision, and F1-score as the metrics to evaluate our model’s performance. In the progress of experiments, our method achieves superior performance over the other classical approaches on the same dataset. The experiments show that YOLOv5’s mAP is 0.010, 0.020, 0.023 higher than those of YOLOv5, Fast RCNN, and EfficientDet. In addition, in the dimension of precision, our model also performs better than other models. The precision of our model is 0.512, which is 0.018, 0.027, 0.033 higher than YOLOv5, Fast RCNN, and EfficientDet.

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Section: Methodsmentioning

confidence: 99%

Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50

Zhang

Sun

et al. 2021

Journal of Healthcare Engineering

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“…It was released in May 2020, a month after the release of YOLOv4. However, the performance of YOLOv5 is higher than the YOLOv4 in terms of both accuracy and speed [30].…”

Section: Object Detectionmentioning

confidence: 93%

Vision-Based Robotic Arm Control Algorithm Using Deep Reinforcement Learning for Autonomous Objects Grasping

et al. 2021

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While working side-by-side, humans and robots complete each other nowadays, and we may say that they work hand in hand. This study aims to evolve the grasping task by reaching the intended object based on deep reinforcement learning. Thereby, in this paper, we propose a deep deterministic policy gradient approach that can be applied to a numerous-degrees-of-freedom robotic arm towards autonomous objects grasping according to their classification and a given task. In this study, this approach is realized by a five-degrees-of-freedom robotic arm that reaches the targeted object using the inverse kinematics method. You Only Look Once v5 is employed for object detection, and backward projection is used to detect the three-dimensional position of the target. After computing the angles of the joints at the detected position by inverse kinematics, the robot’s arm is moved towards the target object’s emplacement thanks to the algorithm. Our approach provides a neural inverse kinematics solution that increases overall performance, and its simulation results reveal its advantages compared to the traditional one. The robot’s end grip joint can reach the targeted location by calculating the angle of every joint with an acceptable range of error. However, the accuracy of the angle and the posture are satisfied. Experiments reveal the performance of our proposal compared to the state-of-the-art approaches in vision-based grasp tasks. This is a new approach to grasp an object by referring to inverse kinematics. This method is not only easier than the standard one but is also more meaningful for multi-degrees of freedom robots.

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“…Yolo is a single stage detection technique without a distinct region proposal and treats the detection of the target as a single regression problem [35]. Object detection using Yolov5 has been demonstrated as a superior way in comparison to other target detection and recognition algorithms [36,37,38,39]. Yolov5 has the advantages of rapid processing time in the deep learning network; ability to handle larger datasets and real-time continuous detection [40,41].…”

Section: Chicktrack Detector Model and Architecturementioning

confidence: 99%

ChickTrack - A Quantitative Tracking Tool for Measuring Chicken Activity

Neethirajan¹

2021

Preprint

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<p>The automatic detection, counting and tracking of individual and flocked chickens in the poultry industry is of paramount to enhance farming productivity and animal welfare. Due to methodological difficulties, such as the complex background of images, varying lighting conditions, and occlusions from e.g., feeding stations, water nipple stations and barriers in the chicken rearing production floor, it is a challenging task to automatically recognize and track birds using computer software. Here, a deep learning model based on You Only Look Once (Yolov5) is proposed for detecting domesticated chickens from videos with varying complex backgrounds. A multiscale feature is being adapted to the Yolov5 network for mapping modules in the counting and tracking of the trajectories of the chickens. The Yolov5 network was trained and tested on our dataset which resulted in an enhanced tracking precision accuracy. Using Kalman Filter, the proposed model was able to track multiple chickens simultaneously with the focus to associate individual chickens across the frames of the video for real time and online applications. By being able to detect the chickens amid diverse background interference and counting them precisely along with tracking the movement and measuring their travelled path and direction, the proposed model provides excellent performance for on-farm applications. Artificial intelligence enabled automatic measurements of chicken behavior on-farm using cameras offers continuous monitoring of the chicken's ability to perch, walk, interact with other birds and the farm environment, as well as the assessment of dustbathing, thigmotaxis, and foraging frequency, which are important indicators for their ability to express natural behaviors. This study highlights the potential of automated monitoring of poultry through the usage of ChickTrack model as a digital tool in enabling science-based animal husbandry practices and thereby promote positive welfare for chickens in animal farming. </p>

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Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

Cited by 146 publications

References 50 publications

Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50

Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50

Vision-Based Robotic Arm Control Algorithm Using Deep Reinforcement Learning for Autonomous Objects Grasping

ChickTrack - A Quantitative Tracking Tool for Measuring Chicken Activity

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