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Fracture is one of the most common and unexpected traumas. If not treated in time, it may cause serious consequences such as joint stiffness, traumatic arthritis, and nerve injury. Using computer vision technology to detect fractures can reduce the workload and misdiagnosis of fractures and also improve the fracture detection speed. However, there are still some problems in sternum fracture detection, such as the low detection rate of small and occult fractures. In this work, the authors have constructed a dataset with 1227 labelled X‐ray images for sternum fracture detection. The authors designed a fully automatic fracture detection model based on a deep convolution neural network (CNN). The authors used cascade R‐CNN, attention mechanism, and atrous convolution to optimise the detection of small fractures in a large X‐ray image with big local variations. The authors compared the detection results of YOLOv5 model, cascade R‐CNN and other state‐of‐the‐art models. The authors found that the convolution neural network based on cascade and attention mechanism models has a better detection effect and arrives at an mAP of 0.71, which is much better than using the YOLOv5 model (mAP = 0.44) and cascade R‐CNN (mAP = 0.55).

Recently, methods based on Faster region-based convolutional neural network (R-CNN) have been popular in multi-class object detection in remote sensing images due to their outstanding detection performance. The methods generally propose candidate region of interests (ROIs) through a region propose network (RPN), and the regions with high enough intersection-over-union (IoU) values against ground truth are treated as positive samples for training. In this paper, we find that the detection result of such methods is sensitive to the adaption of different IoU thresholds. Specially, detection performance of small objects is poor when choosing a normal higher threshold, while a lower threshold will result in poor location accuracy caused by a large quantity of false positives. To address the above issues, we propose a novel IoU-Adaptive Deformable R-CNN framework for multi-class object detection. Specially, by analyzing the different roles that IoU can play in different parts of the network, we propose an IoU-guided detection framework to reduce the loss of small object information during training. Besides, the IoU-based weighted loss is designed, which can learn the IoU information of positive ROIs to improve the detection accuracy effectively. Finally, the class aspect ratio constrained non-maximum suppression (CARC-NMS) is proposed, which further improves the precision of the results. Extensive experiments validate the effectiveness of our approach and we achieve state-of-the-art detection performance on the DOTA dataset.

In smart cities, target detection is one of the major issues in order to avoid traffic congestion. It is also one of the key topics for military, traffic, civilian, sports, and numerous other applications. In daily life, target detection is one of the challenging and serious tasks in traffic congestion due to various factors such as background motion, small recipient size, unclear object characteristics, and drastic occlusion. For target examination, unmanned aerial vehicles (UAVs) are becoming an engaging solution due to their mobility, low cost, wide field of view, accessibility of trained manipulators, a low threat to people’s lives, and ease to use. Because of these benefits along with good tracking effectiveness and resolution, UAVs have received much attention in transportation technology for tracking and analyzing targets. However, objects in UAV images are usually small, so after a neural estimation, a large quantity of detailed knowledge about the objects may be missed, which results in a deficient performance of actual recognition models. To tackle these issues, many deep learning (DL)-based approaches have been proposed. In this review paper, we study an end-to-end target detection paradigm based on different DL approaches, which includes one-stage and two-stage detectors from UAV images to observe the target in traffic congestion under complex circumstances. Moreover, we also analyze the evaluation work to enhance the accuracy, reduce the computational cost, and optimize the design. Furthermore, we also provided the comparison and differences of various technologies for target detection followed by future research trends.

Convolutional neural networks (CNNs) have achieved milestones in object detection of synthetic aperture radar (SAR) images. Recently, vision transformers and their variants have shown great promise in detection tasks. However, ship detection in SAR images remains a substantial challenge because of the characteristics of strong scattering, multi-scale, and complex backgrounds of ship objects in SAR images. This paper proposes an enhancement Swin transformer detection network, named ESTDNet, to complete the ship detection in SAR images to solve the above problems. We adopt the Swin transformer of Cascade-R-CNN (Cascade R-CNN Swin) as a benchmark model in ESTDNet. Based on this, we built two modules in ESTDNet: the feature enhancement Swin transformer (FESwin) module for improving feature extraction capability and the adjacent feature fusion (AFF) module for optimizing feature pyramids. Firstly, the FESwin module is employed as the backbone network, aggregating contextual information about perceptions before and after the Swin transformer model using CNN. It uses single-point channel information interaction as the primary and local spatial information interaction as the secondary for scale fusion based on capturing visual dependence through self-attention, which improves spatial-to-channel feature expression and increases the utilization of ship information from SAR images. Secondly, the AFF module is a weighted selection fusion of each high-level feature in the feature pyramid with its adjacent shallow-level features using learnable adaptive weights, allowing the ship information of SAR images to be focused on the feature maps at more scales and improving the recognition and localization capability for ships in SAR images. Finally, the ablation study conducted on the SSDD dataset validates the effectiveness of the two components proposed in the ESTDNet detector. Moreover, the experiments executed on two public datasets consisting of SSDD and SARShip demonstrate that the ESTDNet detector outperforms the state-of-the-art methods, which provides a new idea for ship detection in SAR images.

Citrus psyllid is the only insect vector of citrus Huanglongbing (HLB), which is the most destructive disease in the citrus industry. There is no effective treatment for HLB, so detecting citrus psyllids as soon as possible is the key prevention measure for citrus HLB. It is time-consuming and laborious to search for citrus psyllids through artificial patrol, which is inconvenient for the management of citrus orchards. With the development of artificial intelligence technology, a computer vision method instead of the artificial patrol can be adopted for orchard management to reduce the cost and time. The citrus psyllid is small in shape and gray in color, similar to the stem, stump, and withered part of the leaves, leading to difficulty for the traditional target detection algorithm to achieve a good recognition effect. In this work, in order to make the model have good generalization ability under outdoor light condition, a high-definition camera to collect data set of citrus psyllids and citrus fruit flies under natural light condition was used, a method to increase the number of small target pests in citrus based on semantic segmentation algorithm was proposed, and the cascade region-based convolution neural networks (R-CNN) (convolutional neural network) algorithm was improved to enhance the recognition effect of small target pests using multiscale training, combining CBAM attention mechanism with high-resolution feature retention network high-resoultion network (HRNet) as feature extraction network, adding sawtooth atrous spatial pyramid pooling (ASPP) structure to fully extract high-resolution features from different scales, and adding feature pyramid networks (FPN) structure for feature fusion at different scales. To mine difficult samples more deeply, an online hard sample mining strategy was adopted in the process of model sampling. The results show that the improved cascade R-CNN algorithm after training has an average recognition accuracy of 88.78% for citrus psyllids. Compared with VGG16, ResNet50, and other common networks, the improved small target recognition algorithm obtains the highest recognition performance. Experimental results also show that the improved cascade R-CNN algorithm not only performs well in citrus psylla identification but also in other small targets such as citrus fruit flies, which makes it possible and feasible to detect small target pests with a field high-definition camera.

Remote sensing image object detection and instance segmentation are widely valued research fields. A convolutional neural network (CNN) has shown defects in the object detection of remote sensing images. In recent years, the number of studies on transformer-based models increased, and these studies achieved good results. However, transformers still suffer from poor small object detection and unsatisfactory edge detail segmentation. In order to solve these problems, we improved the Swin transformer based on the advantages of transformers and CNNs, and designed a local perception Swin transformer (LPSW) backbone to enhance the local perception of the network and to improve the detection accuracy of small-scale objects. We also designed a spatial attention interleaved execution cascade (SAIEC) network framework, which helped to strengthen the segmentation accuracy of the network. Due to the lack of remote sensing mask datasets, the MRS-1800 remote sensing mask dataset was created. Finally, we combined the proposed backbone with the new network framework and conducted experiments on this MRS-1800 dataset. Compared with the Swin transformer, the proposed model improved the mask AP by 1.7%, mask APS by 3.6%, AP by 1.1% and APS by 4.6%, demonstrating its effectiveness and feasibility.

The generation of defects during fabric production impacts fabric quality, and fabric production factories can greatly benefit from the automatic detection of fabric defects. Although object detection algorithms based on convolutional neural networks can be quickly developed, fabric defect detection encounters several problems. Accordingly, a fabric defect detection model based on Cascade R-CNN was proposed in this study. We also proposed block recognition and detection box merging algorithms to achieve complete defect detection in high-resolution images. We implemented a Switchable Atrous Convolution layer to enhance the feature extraction ability of ResNet-50 and upgraded the Feature Pyramid Network to improve the detection accuracy of small defects. Experimental results demonstrated that the proposed model can efficiently perform defect detection in fabric.

At present, bone fracture is a common clinical disease, while the missed diagnosis or misdiagnosis of fracture is harmful to the recovery of patients. Fracture diagnosis often needs the X-ray image as an assistive tool and many fracture detection CAD systems on X-ray images have been explored. However, the majority of existing works mainly focus on detecting fractures in a specific human body part. It’s desirable and feasible to propose a more practical system that can detect various anatomical region fractures ideally due to their similar general fracture characteristics. In this paper, a universal fracture detection CAD system has been developed by us on X-ray images based on the deep learning method. Firstly, we design an image preprocessing method to improve the poor quality of these X-ray images and employ several data augmentation strategies to enlarge the used dataset. Secondly, based on our modified Ada-ResNeSt backbone network and the AC-BiFPN detection method, we propose our automatic fracture detection system. Finally, we establish a private universal fracture detection dataset MURA-D based on the public dataset MURA. As demonstrated by our comprehensive experiments, compared with other popular detectors, our method achieved a higher detection AP of 68.4% with an acceptable inference speed of 122 ms per image on the MURA-D test set, achieving promising results among the state-of-the-art detectors.

Pine trees are more vulnerable to diseases and pests than other trees, so prevention and management are necessary in advance. In this paper, two models of deep learning were mixed to quickly check whether or not to detect pine pests and to perform a comparative analysis with other models. In addition, to select a good performance model of artificial intelligence, a comparison of the recall values, such as Precision (AP), Intersection over Union (IoU) = 0.5, and AP (IoU), of four models including You Only Look Once (YOLOv5s)_Focus+C3, Cascade Region-Based Convolutional Neural Networks (Cascade R-CNN)_Residual Network 50, Faster Region-Based Convolutional Neural Networks, and Faster R-CNN_ResNet50 was performed, and in addition to the mixed model Swin Transformer_Cascade R-CNN proposed in this paper, they were evaluated. As a result of this study, the recall value of the YOLOv5s_Focus+C3 model was 66.8%, the recall value of the Faster R-CNN_ResNet50 model was 91.1%, and the recall value of the Cascade R-CNN_ResNet50 model was 92.9%. The recall value of the model that mixed the Cascade R-CNN_Swin Transformer proposed in this study was 93.5%. Therefore, as a result of comparing the recall values of the performances of the four models in detecting pine pests, the Cascade R-CNN_Swin Transformer mixed model proposed in this paper showed the highest accuracy.