YOLOv5s_2E: Improved YOLOv5s for Aerial Small Target Detection

Shi, Tao; Ding, Yao; Zhu, Wenxu

doi:10.1109/access.2023.3300372

Cited by 8 publications

(2 citation statements)

References 28 publications

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“…The use of focal-EIOU loss in the loss function can boost the weight of high-quality bounding boxes, suppress the weight of low-quality bounding boxes, and solve the problem of imbalance between difficult and easy samples. Similar conclusions have been reached in related studies [31,32].…”

Section: Discussionsupporting

confidence: 92%

Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method

Wang,

Han

et al. 2024

Agriculture

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As strawberries are a widely grown cash crop, the development of strawberry fruit-picking robots for an intelligent harvesting system should match the rapid development of strawberry cultivation technology. Ripeness identification is a key step to realizing selective harvesting by strawberry fruit-picking robots. Therefore, this study proposes combining deep learning and image processing for target detection and classification of ripe strawberries. First, the YOLOv8+ model is proposed for identifying ripe and unripe strawberries and extracting ripe strawberry targets in images. The ECA attention mechanism is added to the backbone network of YOLOv8+ to improve the performance of the model, and Focal-EIOU loss is used in loss function to solve the problem of imbalance between easy- and difficult-to-classify samples. Second, the centerline of the ripe strawberries is extracted, and the red pixels in the centerline of the ripe strawberries are counted according to the H-channel of their hue, saturation, and value (HSV). The percentage of red pixels in the centerline is calculated as a new parameter to quantify ripeness, and the ripe strawberries are classified as either fully ripe strawberries or not fully ripe strawberries. The results show that the improved YOLOv8+ model can accurately and comprehensively identify whether the strawberries are ripe or not, and the mAP50 curve steadily increases and converges to a relatively high value, with an accuracy of 97.81%, a recall of 96.36%, and an F1 score of 97.07. The accuracy of the image processing method for classifying ripe strawberries was 91.91%, FPR was 5.03%, and FNR was 14.28%. This study demonstrates the program’s ability to quickly and accurately identify strawberries at different stages of ripeness in a facility environment, which can provide guidance for selective picking by subsequent fruit-picking robots.

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Section: Discussionsupporting

confidence: 92%

Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method

Wang,

Han

et al. 2024

Agriculture

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“…With the increasing improvement in feature extraction and fusion in network models, some scholars have made enhancements in the post-processing stage of network models. Shi et al [14] proposed an improved YOLOv5s_SE to address the issue of insufficient performance of existing algorithms in detecting small targets. It is achieved by integrating Soft_NMS and EIOU_Loss, replacing the non-maximum suppression function (NMS) in the original network, and thereby improving the detection ability of occluded objects.…”

Section: Introductionmentioning

confidence: 99%

GT-YOLO: Nearshore Infrared Ship Detection Based on Infrared Images

Wang,

Huo

et al. 2024

JMSE

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Traditional visible light target detection is usually applied in scenes with good visibility, while the advantage of infrared target detection is that it can detect targets at nighttime and in harsh weather, thus being able to be applied to ship detection in complex sea conditions all day long. However, in coastal areas where the density of ships is high and there is a significant difference in target scale, this can lead to missed detection of some dense and small targets. To address this issue, this paper proposes an improved detection model based on YOLOv5s. Firstly, this article designs a feature fusion module based on a fusion attention mechanism to enhance the feature fusion of the network and introduces SPD-Conv to improve the detection accuracy of small targets and low-resolution images. Secondly, by introducing Soft-NMS, the detection accuracy is improved while also addressing the issue of missed detections in dense occlusion situations. Finally, the improved algorithm in this article increased mAP0.5 by 1%, mAP0.75 by 5.7%, and mAP0.5:0.95 by 5% on the infrared ship dataset. A large number of comparative experiments have shown that the improved algorithm in this article is effective at improving detection capabilities.

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