Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

Jung, Hyun-Ki; Choi, Gi-Sang

doi:10.3390/app12147255

Cited by 107 publications

(40 citation statements)

References 38 publications

(35 reference statements)

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“…YOLOv5m also outperformed in terms of the loss function. Above all, the study indicates the CC risk factors are really invasive for women who are from developing and underdeveloped countries [36]. In addition to that the performance of all the applied models indicates that all the applied models are capable to identify cancerous cell from cervix images.…”

Section: Loss Analysismentioning

confidence: 72%

“…The target detection architecture relies heavily on the neck. This approach differs from SSD (single-shot detector) [36] in that it does not use a feature layer aggregation process.…”

Section: Yolov5 Neckmentioning

confidence: 99%

“…Furthermore, the suggested term may be used to solve any N-D segmentation problem in any current deep network architecture. The IoU (Intersection over Union) [36] is a typical target detection indicator whose major role is to determine the positive and negative samples as well as to calculate the distance between the output box and the proper label. IoU is scale-invariant, which means it is unaffected by changes in scale.…”

Section: Boundary Loss Functionmentioning

confidence: 99%

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Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5

Ontor¹,

Ali²,

Ahmed³

et al. 2023

Computers, Materials &Amp; Continua

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Cervical Cancer (CC) is a rapidly growing disease among women throughout the world, especially in developed and developing countries. For this many women have died. Fortunately, it is curable if it can be diagnosed and detected at an early stage and taken proper treatment. But the high cost, awareness, highly equipped diagnosis environment, and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early stage. To solve this issue, the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell images. The system is designed using the YOLOv5 (You Only Look Once Version 5) model, which is a deep learning method. To build the model, cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and preprocessed. Then the YOLOv5 models were applied to the labeled dataset to train the model. Four versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early stage. All of the model's variations performed admirably. The model can effectively detect cervical cancerous cell, according to the findings of the experiments. In the medical field, our study will be quite useful. It can be a good option for radiologists and help them make the best selections possible.

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Section: Loss Analysismentioning

confidence: 72%

“…The target detection architecture relies heavily on the neck. This approach differs from SSD (single-shot detector) [36] in that it does not use a feature layer aggregation process.…”

Section: Yolov5 Neckmentioning

confidence: 99%

Section: Boundary Loss Functionmentioning

confidence: 99%

See 1 more Smart Citation

Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5

Ontor¹,

Ali²,

Ahmed³

et al. 2023

Computers, Materials &Amp; Continua

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“…A high detection performance imposes a high demand on the capabilities of the sensor data processing and analysis algorithms, especially if the sensor data are analyzed in an automated manner directly on board the UAV. Changing environmental conditions (e.g., brightness, visibility conditions) as well as variable operational and parameter settings can have a negative impact on sensor data acquisition and the subsequent processing chain, which can ultimately lead to a degradation of the detection performance [ 11 ]. Moreover, it is important to quantitatively determine the confidence in the measurement results.…”

Section: Introductionmentioning

confidence: 99%

Sensor-Model-Based Trajectory Optimization for UAVs to Enhance Detection Performance: An Optimal Control Approach and Experimental Results

Zwick

Gerdts

Stütz

2023

Sensors

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UAVs are widely used for aerial reconnaissance with imaging sensors. For this, a high detection performance (accuracy of object detection) is desired in order to increase mission success. However, different environmental conditions (negatively) affect sensory data acquisition and automated object detection. For this reason, we present an innovative concept that maps the influence of selected environmental conditions on detection performance utilizing sensor performance models. These models are used in sensor-model-based trajectory optimization to generate optimized reference flight trajectories with aligned sensor control for a fixed-wing UAV in order to increase detection performance. These reference trajectories are calculated using nonlinear model predictive control as well as dynamic programming, both in combination with a newly developed sensor performance model, which is described in this work. To the best of our knowledge, this is the first sensor performance model to be used in unmanned aerial reconnaissance that maps the detection performance for a perception chain with a deep learning-based object detector with respect to selected environmental states. The reference trajectory determines the spatial and temporal positioning of the UAV and its imaging sensor with respect to the reconnaissance object on the ground. The trajectory optimization aims to influence sensor data acquisition by adjusting the sensor position, as part of the environmental states, in such a way that the subsequent automated object detection yields enhanced detection performance. Different constraints derived from perceptual, platform-specific, environmental, and mission-relevant requirements are incorporated into the optimization process. We evaluate the capabilities of the sensor performance model and our approach to sensor-model-based trajectory optimization by a series of simulated aerial reconnaissance tasks for ground vehicle detection. Compared to a variety of benchmark trajectories, our approach achieves an increase in detection performance of 4.48% on average for trajectory optimization with nonlinear model predictive control. With dynamic programming, we achieve even higher performance values that are equal to or close to the theoretical maximum detection performance values.

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“…In addition, YOLOv5 consists of four versions on its own, which are YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. This is classified according to the memory storage size, but the principle is the the same [15]. In a similar ways, YOLOv3 is also developed in two small and large scales, YOLOv3s and YOLOv3l respectively.…”

Section: Introductionmentioning

confidence: 99%

YOLO-based Object Detection in Industry 4.0 Fischertechnik Model Environment

Schneidereit¹,

Yarahmadi²,

Schneidereit³

et al. 2023

Preprint

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In this paper we extensively explore the suitability of YOLO architectures to monitor the process flow across a Fischertechnik industry 4.0 application. Specifically, different YOLO architectures in terms of size and complexity design along with different prior-shapes assignment strategies are adopted. To simulate the real world factory environment, we prepared a rich dataset augmented with different distortions that highly enhance and in some cases degrade our image qualities. The degradation is performed to account for environmental variations and enhancements opt to compensate the color correlations that we face while preparing our dataset. The analysis of our conducted experiments shows the effectiveness of the presented approach evaluated using different measures along with the training and validation strategies that we tailored to tackle the unavoidable color correlations that the problem at hand inherits by nature.

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Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

Cited by 107 publications

References 38 publications

Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5

Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5

Sensor-Model-Based Trajectory Optimization for UAVs to Enhance Detection Performance: An Optimal Control Approach and Experimental Results

YOLO-based Object Detection in Industry 4.0 Fischertechnik Model Environment

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