An optimized YOLO‐based object detection model for crop harvesting system

Junos, Mohamad Haniff; Khairuddin, Anis Salwa Mohd; Thannirmalai, Subbiah; Dahari, Mahidzal

doi:10.1049/ipr2.12181

Cited by 56 publications

(24 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, the one-stage detection method integrates region proposal and classification into one step, which reduces the detection time. The mainstream methods of one-stage detection are Single Shot Detector (SSD) and YOLO (27).…”

Section: Background Studymentioning

confidence: 99%

An automated solid waste detection using the optimized YOLO model for riverine management

Zailan

Azizan

Hasikin‬

et al. 2022

Front. Public Health

Self Cite

View full text Add to dashboard Cite

Due to urbanization, solid waste pollution is an increasing concern for rivers, possibly threatening human health, ecological integrity, and ecosystem services. Riverine management in urban landscapes requires best management practices since the river is a vital component in urban ecological civilization, and it is very imperative to synchronize the connection between urban development and river protection. Thus, the implementation of proper and innovative measures is vital to control garbage pollution in the rivers. A robot that cleans the waste autonomously can be a good solution to manage river pollution efficiently. Identifying and obtaining precise positions of garbage are the most crucial parts of the visual system for a cleaning robot. Computer vision has paved a way for computers to understand and interpret the surrounding objects. The development of an accurate computer vision system is a vital step toward a robotic platform since this is the front-end observation system before consequent manipulation and grasping systems. The scope of this work is to acquire visual information about floating garbage on the river, which is vital in building a robotic platform for river cleaning robots. In this paper, an automated detection system based on the improved You Only Look Once (YOLO) model is developed to detect floating garbage under various conditions, such as fluctuating illumination, complex background, and occlusion. The proposed object detection model has been shown to promote rapid convergence which improves the training time duration. In addition, the proposed object detection model has been shown to improve detection accuracy by strengthening the non-linear feature extraction process. The results showed that the proposed model achieved a mean average precision (mAP) value of 89%. Hence, the proposed model is considered feasible for identifying five classes of garbage, such as plastic bottles, aluminum cans, plastic bags, styrofoam, and plastic containers.

show abstract

Section: Background Studymentioning

confidence: 99%

An automated solid waste detection using the optimized YOLO model for riverine management

Zailan

Azizan

Hasikin‬

et al. 2022

Front. Public Health

Self Cite

View full text Add to dashboard Cite

show abstract

“…Compared with K-means clustering, the proposed algorithm can improve the detection accuracy by 1.13% in terms of mAP when using 9 anchors. (9,10), (15,15), (22,23), (31,34), (47,53), (145,178) K-means 9 82.00 (8,9), (12,12), (16,15), (19,20), (24,25), (30,32), (40,44), (51,58), (145,178) 6 81.69 (14,15), (24,27), (44,51), (96,180), (153,146), (187,213) Proposed 9 83.13 (13,13), (19,19), (30,31), (41,50), (50,60), (134,171), (99,197), (157,113), (176,214) Finally, to verify the generality of the proposed scheme, we have also tested the proposed detection framework on the DIOR dataset. As illustrated in Figure 10, the distribution of the obtained anchors by using the proposed scheme is also more evenly allocated than K-means clustering.…”

Section: Improvements By Anchor Configurationmentioning

confidence: 99%

“…The IOU score is further used as the relative distance for anchor configurations in YOLOv4 [ 35 ]. Junos et al [ 47 ] used a similar scheme to optimize the anchor in YOLOv3 and applied the network to the crop harvesting system. Zlocha et al [ 48 ] optimized the anchor configuration based on a differential evolution search algorithm in RetinaNet.…”

Section: Related Workmentioning

confidence: 99%

Fast and Accurate Object Detection in Remote Sensing Images Based on Lightweight Deep Neural Network

Lang

Zhang

et al. 2021

Sensors

View full text Add to dashboard Cite

Deep learning-based object detection in remote sensing images is an important yet challenging task due to a series of difficulties, such as complex geometry scene, dense target quantity, and large variant in object distributions and scales. Moreover, algorithm designers also have to make a trade-off between model’s complexity and accuracy to meet the real-world deployment requirements. To deal with these challenges, we proposed a lightweight YOLO-like object detector with the ability to detect objects in remote sensing images with high speed and high accuracy. The detector is constructed with efficient channel attention layers to improve the channel information sensitivity. Differential evolution was also developed to automatically find the optimal anchor configurations to address issue of large variant in object scales. Comprehensive experiment results show that the proposed network outperforms state-of-the-art lightweight models by 5.13% and 3.58% in accuracy on the RSOD and DIOR dataset, respectively. The deployed model on an NVIDIA Jetson Xavier NX embedded board can achieve a detection speed of 58 FPS with less than 10W power consumption, which makes the proposed detector very suitable for low-cost low-power remote sensing application scenarios.

show abstract

“…Object detection and instance segmentation algorithms are the pillars of many modern computer vision applications as they determine the location and totality of an object within an image, allowing one to carry out more complex tasks such as autonomous navigation [24,25], labor automation [26], and security surveillance [27,28].…”

Section: Related Workmentioning

confidence: 99%

Automated Data Annotation for 6-DoF AI-Based Navigation Algorithm Development

et al. 2021

View full text Add to dashboard Cite

Accurately estimating the six degree of freedom (6-DoF) pose of objects in images is essential for a variety of applications such as robotics, autonomous driving, and autonomous, AI, and vision-based navigation for unmanned aircraft systems (UAS). Developing such algorithms requires large datasets; however, generating those is tedious as it requires annotating the 6-DoF relative pose of each object of interest present in the image w.r.t. to the camera. Therefore, this work presents a novel approach that automates the data acquisition and annotation process and thus minimizes the annotation effort to the duration of the recording. To maximize the quality of the resulting annotations, we employ an optimization-based approach for determining the extrinsic calibration parameters of the camera. Our approach can handle multiple objects in the scene, automatically providing ground-truth labeling for each object and taking into account occlusion effects between different objects. Moreover, our approach can not only be used to generate data for 6-DoF pose estimation and corresponding 3D-models but can be also extended to automatic dataset generation for object detection, instance segmentation, or volume estimation for any kind of object.

show abstract

An optimized YOLO‐based object detection model for crop harvesting system

Cited by 56 publications

References 36 publications

An automated solid waste detection using the optimized YOLO model for riverine management

An automated solid waste detection using the optimized YOLO model for riverine management

Fast and Accurate Object Detection in Remote Sensing Images Based on Lightweight Deep Neural Network

Automated Data Annotation for 6-DoF AI-Based Navigation Algorithm Development

Contact Info

Product

Resources

About