Scale-Sensitive IOU Loss: An Improved Regression Loss Function in Remote Sensing Object Detection

Du, Shuangjiang; Zhang, Baofu; Pin, Zhang

doi:10.1109/access.2021.3119562

Cited by 23 publications

(9 citation statements)

References 31 publications

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“…The IOU distance is calculated as the 1-IOU. For simplicity, this work uses the original IOU definition instead of any novel IOUs 24 , 49 – 51 , which may offer better performance but require more complicated computations. As a result, the value of the BPR improves from 11.4 to 99.9% with the ATT-YOLO design.…”

Section: Methodsmentioning

confidence: 99%

Toward surface defect detection in electronics manufacturing by an accurate and lightweight YOLO-style object detector

Dai

Chen

Líu

et al. 2023

Sci Rep

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In electronics manufacturing, surface defect detection is very important for product quality control, and defective products can cause severe customer complaints. At the same time, in the manufacturing process, the cycle time of each product is usually very short. Furthermore, high-resolution input images from high-resolution industrial cameras are necessary to meet the requirements for high quality control standards. Hence, how to design an accurate object detector with real-time inference speed that can accept high-resolution input is an important task. In this work, an accurate YOLO-style object detector was designed, ATT-YOLO, which uses only one self-attention module, many-scale feature extraction and integration in the backbone and feature pyramid, and an improved auto-anchor design to address this problem. There are few datasets for surface detection in electronics manufacturing. Hence, we curated a dataset consisting of 14,478 laptop surface defects, on which ATT-YOLO achieved 92.8% mAP0.5 for the binary-class object detection task. We also further verified our design on the COCO benchmark dataset. Considering both computation costs and the performance of object detectors, ATT-YOLO outperforms several state-of-the-art and lightweight object detectors on the COCO dataset. It achieves a 44.9% mAP score and 21.8 GFLOPs, which is better than the compared models including YOLOv8-small (44.9%, 28.6G), YOLOv7-tiny-SiLU (38.7%, 13.8G), YOLOv6-small (43.1%, 44.2G), pp-YOLOE-small (42.7%, 17.4G), YOLOX-small (39.6%, 26.8G), and YOLOv5-small (36.7%, 17.2G). We hope that this work can serve as a useful reference for the utilization of attention-based networks in real-world situations.

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Section: Methodsmentioning

confidence: 99%

Toward surface defect detection in electronics manufacturing by an accurate and lightweight YOLO-style object detector

Dai

Chen

Líu

et al. 2023

Sci Rep

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“…Most experiments [31][32][33] have shown that SIoU behaves well in the process of model training. That is the reason why it is introduced into the YOLOv7 model to form the final loss function together with other loss functions, with the formula of loss function exhibited in equation ( 15) where L conf , L cls , and L box and W denote the confidence loss, classification loss, locus box loss and their corresponding weights, respectively.…”

Section: Optimization In the Training Processmentioning

confidence: 99%

An efficient method of pavement distress detection based on improved YOLOv7

Xiao

et al. 2023

Meas. Sci. Technol.

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Pavements play a pivotal role in infrastructure construction, so Pavement Distress Detection (PDD) will greatly affect pavement service life and vehicle operation safety. Traditional manual detection and computer detection methods have such disadvantages as low efficiency, high cost and error-proneness. Thus, they are not suitable for high-speed detection tasks with a large number of defects. The defect detection method based on deep learning can achieve end-to-end target detection, generalize and monitor targets in real time. On such basis, this paper has proposed an efficient method of PDD based on improved YOLOv7. YOLOv7, which is the best-performing object detection model in the YOLO series, is known for its high efficiency, strong scalability, and support for panoramic detection. It lays a solid foundation for enhancing PDD models. In this paper, this model will be improved from model speed and accuracy. Firstly, SimAM attention module is employed to weight feature images, which has greatly improved model accuracy. Secondly, Ghost module in place of partial deep convolution module is used to improve model running speed. Then SIoU, instead of the original localization loss function, is performed to optimize model training process. Finally, the proposed improved YOLOv7 model is applied to different road defect datasets and compared with other methods, such as Faster R-CNN, CenterNet, DETR, YOLOv6 and the original YOLOv7 model. The results show that the proposed method has ubiquitous advantages than the above-mentioned methods, with the average mAP, F1 value and FPS value of 85.8%, 0.697 and 62.13fps respectively. Furthermore, the values of the parameters Params and FLOPs also decrease to some degree.

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“…In object detection, the definition of the loss function has a significant impact on the final performance of the model [25][26][27]. In YOLOX, the GIoU (generalized intersection over union) [28] loss function is used as the localization loss function.…”

Section: Improvement Of Loss Functionmentioning

confidence: 99%

Substation Personnel Fall Detection Based on Improved YOLOX

Fan,

Gong,

Fan

et al. 2023

Electronics

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With the continuous promotion of smart substations, staff fall detection has become a key issue in automatic detection of substations. The injuries and safety hazards caused by falls among substation personnel are numerous. If a timely response can be made in the event of a fall, the injuries caused by falls can be reduced. In order to address the issues of low accuracy and poor real-time performance in detecting human falls in complex substation scenarios, this paper proposes an improved algorithm based on YOLOX. A customized feature extraction module is introduced to the YOLOX feature fusion network to extract diverse multiscale features. A recursive gated convolutional module is added to the head to enhance the expressive power of the features. Meanwhile, the SIoU(Soft Intersection over Union) loss function is utilized to provide more accurate position information for bounding boxes, thereby improving the model accuracy. Experimental results show that the improved algorithm achieves an mAP value of 78.45%, which is a 1.31% improvement over the original YOLOX. Compared to other similar algorithms, the proposed algorithm achieves high accuracy prediction of human falls with fewer parameters, demonstrating its effectiveness.

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Scale-Sensitive IOU Loss: An Improved Regression Loss Function in Remote Sensing Object Detection

Cited by 23 publications

References 31 publications

Toward surface defect detection in electronics manufacturing by an accurate and lightweight YOLO-style object detector

Toward surface defect detection in electronics manufacturing by an accurate and lightweight YOLO-style object detector

An efficient method of pavement distress detection based on improved YOLOv7

Substation Personnel Fall Detection Based on Improved YOLOX

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