ECAP-YOLO: Efficient Channel Attention Pyramid YOLO for Small Object Detection in Aerial Image

Kim, Munhyeong; Jeong, Jongmin; Kim, Sungho

doi:10.3390/rs13234851

Cited by 82 publications

(39 citation statements)

References 22 publications

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“…Nevertheless, as the core of our scheme, the detector is weak in detecting small targets (Figure 14 and Table 2). Due to lower resolution, fewer features, and more noise, small object detection is one of the challenging problems in object detection [42]. For further research, we will focus on two aspects.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A Vision-Based Detection and Spatial Localization Scheme for Forest Fire Inspection from UAV

et al. 2022

Forests

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Forest fires have the characteristics of strong unpredictability and extreme destruction. Hence, it is difficult to carry out effective prevention and control. Once the fire spreads, devastating damage will be caused to natural resources and the ecological environment. In order to detect early forest fires in real-time and provide firefighting assistance, we propose a vision-based detection and spatial localization scheme and develop a system carried on the unmanned aerial vehicle (UAV) with an OAK-D camera. During the high incidence of forest fires, UAVs equipped with our system are deployed to patrol the forest. Our scheme includes two key aspects. First, the lightweight model, NanoDet, is applied as a detector to identify and locate fires in the vision field. Techniques such as the cosine learning rate strategy and data augmentations are employed to further enhance mean average precision (mAP). After capturing 2D images with fires from the detector, the binocular stereo vision is applied to calculate the depth map, where the HSV-Mask filter and non-zero mean method are proposed to eliminate the interference values when calculating the depth of the fire area. Second, to get the latitude, longitude, and altitude (LLA) coordinates of the fire area, coordinate frame conversion is used along with data from the GPS module and inertial measurement unit (IMU) module. As a result, we experiment with simulated fire in a forest area to test the effectiveness of this system. The results show that 89.34% of the suspicious frames with flame targets are detected and the localization error of latitude and longitude is in the order of 10−5 degrees; this demonstrates that the system meets our precision requirements and is sufficient for forest fire inspection.

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Section: Conclusion and Discussionmentioning

confidence: 99%

A Vision-Based Detection and Spatial Localization Scheme for Forest Fire Inspection from UAV

et al. 2022

Forests

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“…In Section 4, All of these methods are compared with our proposed method, called RVD-YOLOv5. Using the MobileNet architecture to generate the base convolutional layer in Faster R-CNN, Kim et al [24] have presented an improved Faster R-CNN technique for fast vehicle detection. In this method, the soft NMS algorithm replaced the NMS algorithm for solving the issue of duplicate proposals.…”

Section: Related Workmentioning

confidence: 99%

“…Object detection is a technique based on computer vision that detects the semantic objects instances class of objects [1]. Kim et al [2] defined object detection as "object detection combining the multi-labelled classification and bounding box regression", where assigning class level and drawing the bounding box for each object refers to image classification and object localization. The rapid growth in vehicles on the road has significantly attracted researchers' attention to traffic safety issues.…”

Section: Introductionmentioning

confidence: 99%

Robust Vehicle Detection Based on Improved You Look Only Once

Kumar¹,

Jailia²,

Varshney³

et al. 2023

Computers, Materials &Amp; Continua

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Vehicle detection is still challenging for intelligent transportation systems (ITS) to achieve satisfactory performance. The existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection performance. Due to advancements in detection technology, deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing algorithms. This paper presents a robust vehicle detection technique based on Improved You Look Only Once (RVD-YOLOv5) to enhance vehicle detection accuracy. The proposed method works in three phases; in the first phase, the K-means algorithm performs data clustering on datasets to generate the classes of the objects. Subsequently, in the second phase, the YOLOv5 is applied to create the bounding box, and the Non-Maximum Suppression (NMS) technique is used to eliminate the overlapping of the bounding boxes of the vehicle. Then, the loss function CIoU is employed to obtain the accurate regression bounding box of the vehicle in the third phase. The simulation results show that the proposed method achieves better results when compared with other state-of-art techniques, namely Lightweight Dilated Convolutional Neural Network (LD-CNN), Single Shot Detector (SSD), YOLOv3 and YOLOv4 on the performance metric like precision, recall, mAP and F1-Score. The simulation and analysis are carried out on PASCAL VOC 2007, 2012 and MS COCO 2017 datasets to obtain better performance for vehicle detection. Finally, the RVD-YOLOv5 obtains the results with an mAP of 98.6% and Precision, Recall, and F1-Score are 98%, 96.2% and 97.09%, respectively.

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“…Adding the shallow characteristics of the high-resolution layer has become one of the methods for small target detection. That is, the shallow feature map in the backbone module of Figure 1 b is integrated into the neck module of Figure 1 c. Kim et al [ 14 ] proposed the structure of ECAP-YOLO to improve the detection performance of small targets in aerial photography scenes. Shao et al [ 15 ] proposed an adaptive spatial feature fusion network with a high-resolution detection layer to enhance the effect of ship detection in night remote sensing scenes.…”

Section: Related Workmentioning

confidence: 99%

CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection

Yuan

Qian

et al. 2022

Sensors

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Infrared ocean ships detection still faces great challenges due to the low signal-to-noise ratio and low spatial resolution resulting in a severe lack of texture details for small infrared targets, as well as the distribution of the extremely multiscale ships. In this paper, we propose a CAA-YOLO to alleviate the problems. In this study, to highlight and preserve features of small targets, we apply a high-resolution feature layer (P2) to better use shallow details and the location information. In order to suppress the shallow noise of the P2 layer and further enhance the feature extraction capability, we introduce a TA module into the backbone. Moreover, we design a new feature fusion method to capture the long-range contextual information of small targets and propose a combined attention mechanism to enhance the ability of the feature fusion while suppressing the noise interference caused by the shallow feature layers. We conduct a detailed study of the algorithm based on a marine infrared dataset to verify the effectiveness of our algorithm, in which the AP and AR of small targets increase by 5.63% and 9.01%, respectively, and the mAP increases by 3.4% compared to that of YOLOv5.

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ECAP-YOLO: Efficient Channel Attention Pyramid YOLO for Small Object Detection in Aerial Image

Cited by 82 publications

References 22 publications

A Vision-Based Detection and Spatial Localization Scheme for Forest Fire Inspection from UAV

A Vision-Based Detection and Spatial Localization Scheme for Forest Fire Inspection from UAV

Robust Vehicle Detection Based on Improved You Look Only Once

CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection

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