CISPNet: Automatic Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Context Information Scene Perception

We propose a lightweight detection algorithm based on the Single Shot MultiBox Detector (SSD) algorithm in order to facilitate sheep management and to realize sheep facial identification, and we take the self-constructed dataset as the research object. First, the SSD replaces the VGG16 backbone network with MobileNetv3, a lightweight neural network, to create a hybrid model that is much smaller. Second, the ECA attention mechanism is incorporated into the backend of the 72 × 160 bottleneck layer. Finally, the SmoothL1 loss function is substituted with the BalancedL1 loss function. The optimized model’s size decreases significantly from the original SSD’s 132 MB to just 22.4 MB. It achieves a mean average precision of 83.47% and maintains an average frame rate of 68.53 frames per second. Compared to the basic SSD model, the mean average precision has increased by 3.25 percentage points, the model size has decreased by 109.6 MB, and the detection speed has improved by 9.55 frames per second. In comparative experiments using the same dataset with different object detection models, the proposed model outperforms the SSD, Faster R-CNN, Retinanet, and CenterNet in terms of mean average precision, with improvements of 3.25 percentage points, 4.71 percentage points, 2.38 percentage points, and 8.13 percentage points, respectively. The detection speed has shown significant improvements, increasing by 9.55, 58.55, 53.1, and 12.37 frames per second, respectively. The improved model presented in this paper significantly reduces the model’s size and computational requirements while maintaining an excellent performance. This provides a valuable reference for the digitalization of animal husbandry and livestock farming.

Aiming at the problem of target frame redundancy in the original Single Shot MultiBox Detector (SSD) model, this paper proposes a Cervical epithelial cell recognition based on improved SSD. The algorithm uses the mask to extract the ROI region, and preprocesses the original image, so that the computational complexity of the extracted image features is greatly reduced, and then the resnet network is used to replace the original VGG network. Finally, the modified SSD model is used to perform cervical epithelial cells. Identify and accurately locate. The algorithm proposed in this paper can accurately and quickly excavate the characteristics of cervical epithelial cells under microscope, and effectively improve the recognition speed and target positioning accuracy.

Single shot multibox detector (SSD) is a state-of-the-art network for real-time object detection. It is originally designed for general datasets. While, for specific datasets, their distribution of ground truth boxes is somehow different and thus, SSD shows unsatisfying performance. In this paper, we improve the performance of SSD on specific datasets. We first dissect the mechanism of predictors, the predicting parameters of a potential detection, in two aspects: classification and localization. Then we reveal the relationship between default boxes and predictors. With this point we finally make an improvement on default box setting and achieve a higher mAP over the original SSD on specific datasets.

In recent years, with the rapid development of satellite technology, object detection technology in remote sensing images has become an important research direction in the field of image processing. In this paper, aiming at the problems of low resolution, slow detection speed and vulnerability to background interference in remote sensing satellite images of ground vehicles, a vehicle small object detection algorithm based on residual network is proposed. Based on the original SSD (Single Shot MultiBox Detector) algorithm, the backbone network adopts the residual network with stronger feature extraction ability and less parameters, which can effectively improve the detection accuracy of small targets; Meanwhile, the Dense Extra Feature Layers network is proposed to enhance Feature propagation. Experimental results show that the mAP value of the proposed method in vehicle remote sensing image detection reaches 0.573, and the detection speed is 30.1fps, which has a good improvement in the detection accuracy and speed.

We propose an object detection method that improves the accuracy of the conventional SSD (Single Shot Multibox Detector), which is one of the top object detection algorithms in both aspects of accuracy and speed. The performance of a deep network is known to be improved as the number of feature maps increases. However, it is difficult to improve the performance by simply raising the number of feature maps. In this paper, we propose and analyze how to use feature maps effectively to improve the performance of the conventional SSD. The enhanced performance was obtained by changing the structure close to the classifier network, rather than growing layers close to the input data, e.g., by replacing VGGNet with ResNet. The proposed network is suitable for sharing the weights in the classifier networks, by which property, the training can be faster with better generalization power. For the Pascal VOC 2007 test set trained with VOC 2007 and VOC 2012 training sets, the proposed network with the input size of 300 × 300 achieved 78.5% mAP (mean average precision) at the speed of 35.0 FPS (frame per second), while the network with a 512 × 512 sized input achieved 80.8% mAP at 16.6 FPS using Nvidia Titan X GPU. The proposed network shows state-of-the-art mAP, which is better than those of the conventional SSD, YOLO, Faster-RCNN and RFCN. Also, it is faster than Faster-RCNN and RFCN.

An intelligent water level monitoring method based on SSD algorithm

The fabric defect detection algorithm based on object detection has become a research hotspot. The method based on the Single Shot MultiBox Detector (SSD) model has a fast detection speed, but the detection accuracy is insufficient. To balance the detection speed and accuracy of the model and meet the actual needs of the industry, an improved fabric defect detection algorithm based on SSD is proposed in this study. The Fully Convolutional Squeeze-and-Excitation (FCSE) block is added into the traditional SSD to improve the detection accuracy of the model. The number of default boxes was adjusted to accommodate the detection of long strip defects on fabric surface. Experimental results on the TILDA and Xuelang dataset confirm that our detection method based on SSD efficiently detected various fabric defects.

Image noise is a prevalent issue often created by inadequate lighting, cameras of low quality, compression of images and other factors. While low image quality is anticipated to degrade visual identification outcomes, most present object recognition techniques and benchmarks, such as Pascal Visual Object Classes Challenge and Microsoft Common Objects in Context Challenge, concentrate on images of comparatively high quality. Meanwhile, the identification of objects in noisy images in surveillance and other fields is a prevalent issue. In this paper we discuss the detection of objects in noisy images and suggest a new low-cost technique to detect image objects from noisy images. Receiving the benefits of the Single Shot MultiBox Detector (SSD), we have presented an extensive experimental assessment with conventional detectors retrained on noisy images. Results are provided for the prevalent benchmark for object detection Pascal Visual Object Classes. Comparing with other image detection approaches, our technique has provided satisfactory performance at the time of detecting image objects from noisy images. The method can be very effective to the autonomous industries to resolve the object detection related difficulties created by insufficient lighting and lower regulation pictures.

Lightweight target detection for the field flat jujube based on improved YOLOv5

With the development of the new generation communication system in China, the application of intelligent transportation system is more extensive, which brings higher demands for vehicle flow detection and monitoring. Traditional traffic flow detection modes often cannot meet the high statistical accuracy requirement and high-speed detection simultaneously. Therefore, an improved Inception module is integrated into the single shot multi box detector algorithm. An intelligent vehicle flow detection model is constructed based on the improved single shot multi box detector algorithm. According to the findings, the convergence speed of the improved algorithm was the fastest. When the test sample was the entire test set, the accuracy and precision values of the improved method were 93.6% and 96.0%, respectively, which were higher than all comparison target detection algorithms. The experimental results of traffic flow statistics showed that the model had the highest statistical accuracy, which converged during the training phase. During the testing phase, except for manual statistics, all methods had the lowest statistical accuracy on motorcycles. The average accuracy and precision of the designed model for various types of images were 96.9% and 96.8%, respectively. The calculation speed of this intelligent model was not significantly improved compared to the other two intelligent models, but it was significantly higher than manual monitoring methods. Two experimental data demonstrate that the intelligent vehicle flow detection model designed in this study has higher detection accuracy. The calculation speed has no significant difference compared with the traditional method, which is helpful to the traffic flow management in intelligent transportation system.

Purpose In order to resolve the situation of high missed diagnosis rate and high misdiagnosis rate of the pathological analysis of the gastrointestinal endoscopic images by experts, we propose an automatic polyp detection algorithm based on Single Shot Multibox Detector (SSD). Method In the paper, SSD is based on VGG-16, the fully connected layer is changed to a convolutional layer, and four convolutional layers with successively decreasing scales are added as a new network structure. In order to verify the practicability, it is not only compared with manual polyp detection but also with Mask R-CNN. Results Multiple experimental results show that the mean Average Precision (mAP) of the SSD network is 95.74%, which is 12.4% higher than the manual detection and 5.7% higher than the Mask R-CNN. When detecting a single frame of image, the detection speed of SSD is 8.41 times that of manual detection. Conclusion Based on the traditional pattern recognition algorithm and the target detection algorithm using deep learning, we select a variety of algorithms to identify and classify polyps to achieve efficient detection results. Our research demonstrates that deep learning has a lot of room for development in the field of gastrointestinal image recognition.

Timber transportation surveillance is the key and important means of forest resource management. In order to surveille timber transportation in the forest road, we carry out timber transportation vehicle detection from the video of bayonet, with which we propose an improvement of SSD (Single Shot Multibox Detector) based on MXNet. We establish a timber transportation vehicle dataset for training and testing the ResNet50 and Darknet53 with three thresholds. As mAP (mean Average Precision) is closely related to IoU (Intersection over Union), the original calculation of which does not consider overlap and disjoint between prediction box and the ground truth, we find it contributes the major part of error-detection with SSD. Inspired by this, we combine a novel metric loss calculation called GIoU (Generalized Intersection over the Union) with SSD to improve mAP, which is proved to be greatly promoted in timber transportation vehicle detection.

The single shot multi-box detector (SSD) exhibits low accuracy in small-object detection; this is because it does not consider the scale contextual information between its layers, and the shallow layers lack adequate semantic information. To improve the accuracy of the original SSD, this paper proposes a new single shot multi-box detector using trident feature and squeeze and extraction feature fusion (SSD-TSEFFM); this detector employs the trident network and the squeeze and excitation feature fusion module. Furthermore, a trident feature module (TFM) is developed, inspired by the trident network, to consider the scale contextual information. The use of this module makes the proposed model robust to scale changes owing to the application of dilated convolution. Further, the squeeze and excitation block feature fusion module (SEFFM) is used to provide more semantic information to the model. The SSD-TSEFFM is compared with the faster regions with convolution neural network features (RCNN) (2015), SSD (2016), and DF-SSD (2020) on the PASCAL VOC 2007 and 2012 datasets. The experimental results demonstrate the high accuracy of the proposed model in small-object detection, in addition to a good overall accuracy. The SSD-TSEFFM achieved 80.4% mAP and 80.2% mAP on the 2007 and 2012 datasets, respectively. This indicates an average improvement of approximately 2% over other models.

With the ever-increasing demand for marine products with high nutritional value in the sea, rapid and accurate detection of target organisms is highly desired. In this context, a novel underwater object detection algorithm based on improved Single Shot MultiBox Detector (SSD) is proposed to improve the detection accuracy and real-time performance in complex underwater environment. The proposed algorithm employs ResNeXt-50 instead of VGGNet-16 as the backbone network to improve the ability to obtain details and position information of the target object. To be more specific, a channel-spatial attention mechanism is incorporated in the deep layers of the improved XC-SSD model to enhance semantic information of high-level feature maps and reduce missed detection rate and false detection rate. Comprehensive experiments on the URPC-2019 dataset show that the detection accuracy and speed of the proposed algorithm are 79.76% and 18.95 FPS, respectively, which are 3.49% and 2.8 FPS higher than the original SSD algorithm. Experimental results clearly demonstrate that the proposed algorithm is highly efficient and suitable for real-time object detection.

The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection. This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.