YOLOv5 Deep Neural Network for Quince and Raspberry Detection on RGB Images

Sudars, Kaspars; Namatēvs, Ivars; Judvaitis, Jānis; Balašs, Rihards; Ņikuļins, Artūrs; Peter, Astile; Strautina, S.; Kaufmane, E.; Kalniņa, Ieva

doi:10.1109/mttw56973.2022.9942550

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…The algorithm proposed in this paper uses the YOLOv5 network to create a model for detecting weak impurity points in edible oils. The model comprises three components: a backbone feature extraction layer, a feature fusion layer, and an object detection layer [27]. The network architecture is shown in Figure 4.…”

Section: Yolov5 Object Detection Networkmentioning

confidence: 99%

Event-Based Weak Oil Impurity Detection

Li,

Wang,

Chen

et al. 2024

IEEE Access

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Machine vision-based methods are usually applied to detect impurities in transparent liquids like water or alcohols. Oils have higher viscosity and lower light transmittance, so traditional vision detection methods do not work well. A weak oil impurity detection algorithm was proposed using event stream data. In this method, the binary image of the oil was firstly captured using an event camera. To reduce the noise interference from the event camera, image filtering and morphological operations were applied. Then, image algebra operations were used to reduce the oil container's bottom pattern. Finally, impurity detection was performed through the YOLOv5 network. Three common edible oils serve as the experimental samples. Small flying insects, raw material fragments, metal fragments, hair strands, and tin beads of various sizes are selected as the weak impurities. Experiments were performed on a dataset containing 3000 sample images. To the best of our knowledge, existing algorithms can detect impurities with the minimum size of 0.4mm, and most of the experimental samples are transparent liquids. The proposed method can be applied to detect impurities in water and oils, and the detectable impurity size limit is 0.2mm.INDEX TERMS Impurity detection, dynamic vision, event camera.

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Section: Yolov5 Object Detection Networkmentioning

confidence: 99%

Event-Based Weak Oil Impurity Detection

Li,

Wang,

Chen

et al. 2024

IEEE Access

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“…However, when dealing with limited resources, such as the Raspberry Pi, the primary considerations revolve around the inference speed and the model accuracy. YOLOv5 has demonstrated a trade-off between speed and accuracy in various detection applications compared to other approaches [54,55]. Moreover, it is well-suited for resource-constrained environments due to its low parameters within the model weights.…”

Section: Object Detectionmentioning

confidence: 99%

Anomaly Detection on the Edge Using Smart Cameras under Low-Light Conditions

Abu Awwad,

Rana,

Perera

2024

Sensors

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The number of cameras utilised in smart city domains is increasingly prominent and notable for monitoring outdoor urban and rural areas such as farms and forests to deter thefts of farming machinery and livestock, as well as monitoring workers to guarantee their safety. However, anomaly detection tasks become much more challenging in environments with low-light conditions. Consequently, achieving efficient outcomes in recognising surrounding behaviours and events becomes difficult. Therefore, this research has developed a technique to enhance images captured in poor visibility. This enhancement aims to boost object detection accuracy and mitigate false positive detections. The proposed technique consists of several stages. In the first stage, features are extracted from input images. Subsequently, a classifier assigns a unique label to indicate the optimum model among multi-enhancement networks. In addition, it can distinguish scenes captured with sufficient light from low-light ones. Finally, a detection algorithm is applied to identify objects. Each task was implemented on a separate IoT-edge device, improving detection performance on the ExDark database with a nearly one-second response time across all stages.

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“…The Backbone section is mainly composed of the downsampling module (CBS), the residual module (C3) and the SPPF (Spatial Pyramid Pooling-Fast) 58 . One of these is the CBS module 59 which is a standard convolution module. It is made up of the convolution operation, the batch norm normalisation process and the SiLU activation function 60 .…”

Section: Theoretical Workmentioning

confidence: 99%

“…Anchor frame size Tiny detection head [3,4], [4,9], [8,6] Small detection head [7,13], [13,9], [12,17] Medium detection head [23,13], [19,23], [41,21] Large detection head [27,44], [59,40], [80,86] Table 1. Anchor frame size for the four inspection heads of the GBS-YOLOv5 model…”

Section: Detection Headmentioning

confidence: 99%

GBS-YOLOv5: Improved Algorithm for UAV Intelligent Transportation Based on YOLOv5

Liu

Duan

Lou

et al. 2023

Preprint

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As the road traffic situation becomes complex, the task of traffic management takes on an increasingly heavy load. The air-to-ground traffic administration network of drones has become an important tool to promote the high quality of traffic police work in many places. Drones can be used instead of a large number of human beings to perform daily tasks, as: traffic offense detection, daily crowd detection, etc. UAVs are less accurate at detecting drones because they operate from the air and take pictures of small targets. To address the problem of low accuracy of UAVs in detecting small targets, we proposed the GBS-YOLOv5 model. It was an improvement on the original YOLOv5 model. The default YOLOv5 model suffered from severe loss of small target information and under-utilization of shallow feature information as the depth of the feature extraction network deepened. We deepened the feature extraction network by introducing an efficient spatiotemporal interaction (ESI) module. We added the spatial pyramid convolution (SPC) module as a detection head for tiny-sized targets. A shallow bottleneck (SB) was introduced to better preserve the detailed information of small targets in the shallow features. The introduction of recursive gated convolution in the feature fusion section enabled better interaction of higher-order spatial semantic information. The GBS-YOLOv5 algorithm conducted experiments showing that the value of mAP@0.5 was 35.3% and the mAP@0.5:0.95 was 20.0%. Compared to the default YOLOv5 algorithm was boosted by 4.0% and 3.5% respectively.

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YOLOv5 Deep Neural Network for Quince and Raspberry Detection on RGB Images

Cited by 9 publications

References 4 publications

Event-Based Weak Oil Impurity Detection

Event-Based Weak Oil Impurity Detection

Anomaly Detection on the Edge Using Smart Cameras under Low-Light Conditions

GBS-YOLOv5: Improved Algorithm for UAV Intelligent Transportation Based on YOLOv5

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