Deep Learning Object Detection Methods Research Articles

GinJinn: An object-detection pipeline for automated feature extraction from herbarium specimens.

PremiseThe generation of morphological data in evolutionary, taxonomic, and ecological studies of plants using herbarium material has traditionally been a labor‐intensive task. Recent progress in machine learning using deep artificial neural networks (deep learning) for image classification and object detection has facilitated the establishment of a pipeline for the automatic recognition and extraction of relevant structures in images of herbarium specimens.Methods and ResultsWe implemented an extendable pipeline based on state‐of‐the‐art deep‐learning object‐detection methods to collect leaf images from herbarium specimens of two species of the genus Leucanthemum. Using 183 specimens as the training data set, our pipeline extracted one or more intact leaves in 95% of the 61 test images.ConclusionsWe establish GinJinn as a deep‐learning object‐detection tool for the automatic recognition and extraction of individual leaves or other structures from herbarium specimens. Our pipeline offers greater flexibility and a lower entrance barrier than previous image‐processing approaches based on hand‐crafted features.

Moth Detection from Pheromone Trap Images Using Deep Learning Object Detectors

Diverse pheromones and pheromone-based traps, as well as images acquired from insects captured by pheromone-based traps, have been studied and developed to monitor the presence and abundance of pests and to protect plants. The purpose of this study is to construct models that detect three species of pest moths in pheromone trap images using deep learning object detection methods and compare their speed and accuracy. Moth images in pheromone traps were collected for training and evaluation of deep learning detectors. Collected images were then subjected to a labeling process that defines the ground truths of target objects for their box locations and classes. Because there were a few negative objects in the dataset, non-target insects were labeled as unknown class and images of non-target insects were added to the dataset. Moreover, data augmentation methods were applied to the training process, and parameters of detectors that were pre-trained with the COCO dataset were used as initial parameter values. Seven detectors—Faster R-CNN ResNet 101, Faster R-CNN ResNet 50, Faster R-CNN Inception v.2, R-FCN ResNet 101, Retinanet ResNet 50, Retinanet Mobile v.2, and SSD Inception v.2 were trained and evaluated. Faster R-CNN ResNet 101 detector exhibited the highest accuracy (mAP as 90.25), and seven different detector types showed different accuracy and speed. Furthermore, when unexpected insects were included in the collected images, a four-class detector with an unknown class (non-target insect) showed lower detection error than a three-class detector.

Automatic defect detection based on improved Faster RCNN for substation equipment

Defect detection methods based on machine learning extremely accelerate the substation routine inspection process. In this paper, we propose an automatic defect detection method based on improved Faster RCNN. For one thing, random feature pyramid (RFP) structure is introduced for the highly discriminative feature map construction; for another thing, we execute the detection boxes selection by soft non-maximum suppression (SNMS), keeping the detection of defects which distribute densely. Finally, online hard example mining (OHEM) is employed to deal with the imbalance problem. Experimental results demonstrate that the proposed approach obtains competitive performance compared with state-of-the-art deep learning object detection methods.

Automatic bird song and syllable segmentation with an open-source deep-learning object detection method – a case study in the Collared Flycatcher (Ficedula albicollis)

Abstract The bioacoustic analyses of animal sounds result in an enormous amount of digitized acoustic data, and we need effective automatic processing to extract the information content of the recordings. Our research focuses on the song of Collared Flycatcher (Ficedula albicollis) and we are interested in the evolution of acoustic signals. During the last 20 years, we obtained hundreds of hours of recordings of bird songs collected in natural environment, and there is a permanent need for the automatic process of recordings. In this study, we chose an open-source, deep-learning image detection system to (1) find the species-specific songs of the Collared Flycatcher on the recordings and (2) to detect the small, discrete elements so-called syllables within the song. For these tasks, we first transformed the acoustic data into spectrogram images, then we trained two deep-learning models separately on our manually segmented database. The resulted models detect the songs with an intersection of union higher than 0.8 and the syllables higher than 0.7. This technique anticipates an order of magnitude less human effort in the acoustic processing than the manual method used before. Thanks to the new technique, we are able to address new biological questions that need large amount of acoustic data.

Early esophageal adenocarcinoma detection using deep learning methods

PurposeThis study aims to adapt and evaluate the performance of different state-of-the-art deep learning object detection methods to automatically identify esophageal adenocarcinoma (EAC) regions from high-definition white light endoscopy (HD-WLE) images.MethodSeveral state-of-the-art object detection methods using Convolutional Neural Networks (CNNs) were adapted to automatically detect abnormal regions in the esophagus HD-WLE images, utilizing VGG’16 as the backbone architecture for feature extraction. Those methods are Regional-based Convolutional Neural Network (R-CNN), Fast R-CNN, Faster R-CNN and Single-Shot Multibox Detector (SSD). For the evaluation of the different methods, 100 images from 39 patients that have been manually annotated by five experienced clinicians as ground truth have been tested.ResultsExperimental results illustrate that the SSD and Faster R-CNN networks show promising results, and the SSD outperforms other methods achieving a sensitivity of 0.96, specificity of 0.92 and F-measure of 0.94. Additionally, the Average Recall Rate of the Faster R-CNN in locating the EAC region accurately is 0.83.ConclusionIn this paper, recent deep learning object detection methods are adapted to detect esophageal abnormalities automatically. The evaluation of the methods proved its ability to locate abnormal regions in the esophagus from endoscopic images. The automatic detection is a crucial step that may help early detection and treatment of EAC and also can improve automatic tumor segmentation to monitor its growth and treatment outcome.

Assessing Deep-learning Methods for Object Detection at Sea from LWIR Images

This paper assesses the performance of three convolutional neural networks for object detection at sea using Long Wavelength Infrared (LWIR) images in the 8- 14µm range. Capturing images from ferries and annotating 20k images, fine-tuning is done of three state of art deep neural networks: RetinaNet, YOLO and Faster R-CNN. Targeting on vessels and buoys as two main classes of interest for navigation, performance is quantified by the cardinality of true and false positives and negatives in a random validation set. Calculating precision and recall as functions of tuning parameters for the three classifiers, noticeable differences are found between the three networks when used for LWIR image object classification at sea. The results lead to conclusions on imaging requirements when classification is used to support navigation.