31 research outputs found
Surveying wildlife and livestock in Uganda with aerial cameras:Deep Learning reduces the workload of human interpretation by over 70%
As the need to accurately monitor key-species populations grows amid increasing pressures on global biodiversity, the counting of large mammals in savannas has traditionally relied on the Systematic-Reconnaissance-Flight (SRF) technique using light aircrafts and human observers. However, this method has limitations, including non-systematic human errors. In recent years, the Oblique-Camera-Count (OCC) approach developed in East Africa has utilized cameras to capture high-resolution imagery replicating aircraft observers’ oblique view. Whilst demonstrating that human observers have missed many animals, OCC relies on labor-intensive human interpretation of thousands of images. This study explores the potential of Deep Learning (DL) to reduce the interpretation workload associated with OCC surveys. Using oblique aerial imagery of 2.1 hectares footprint collected during an SRF-OCC survey of Queen Elizabeth Protected Area in Uganda, a DL model (HerdNet) was trained and evaluated to detect and count 12 wildlife and livestock mammal species. The model’s performance was assessed both at the animal instance-based and image-based levels, achieving accurate detection performance (F1 score of 85%) in positive images (i.e. containing animals) and reducing manual interpretation workload by 74% on a realistic dataset showing less than 10% of positive images. However, it struggled to differentiate visually related species and overestimated animal counts due to false positives generated by landscape items resembling animals. These challenges may be addressed through improved training and verification processes. The results highlight DL’s potential to semi-automate processing of aerial survey wildlife imagery, reducing manual interpretation burden. By incorporating DL models into existing counting standards, future surveys may increase sampling efforts, improve accuracy, and enhance aerial survey safety.</p
Towards the automation of large mammal aerial survey in Africa
editorial reviewedIn African open protected areas, large mammals are often surveyed using manned aircrafts which actively count the animals in sample strips for later density extrapolation to the whole area. Nevertheless, this method may be biased among others by the observer’s detection capability. The use of on-board oblique cameras has recently shown an increase in counting accuracy as a result of indirect photo-interpretation. While this approach appears to reduce some biases, the processing time of the generated data is currently a bottleneck. In recent years, Deep Learning (DL) techniques through dense convolutional neural networks (CNNs) have emerged as a very promising avenue for managing such datasets. However, we are not yet at the stage of full automation of the process (i.e. from acquisition to population estimation). Three challenges were identified: 1) reducing false positives, 2) increasing the precision in close-by individuals, and 3) properly managing the overlap between images to avoid double counting. We focused on the two first aspects and developed a new point-based DL model inspired by crowd counting, that was applied on a challenging oblique aerial dataset containing free ranging livestock herds in heterogeneous open arid landscapes. The model’s performances were then evaluated using localization and counting metrics. The DL model achieved a global F1 score of 0.74 and a RMSE of 9.8 animals per 24 megapixel image, at a processing speed of 3.6 s/image. It showed a valuable ability to detect both isolated animals and those in dense herds. This is auspicious for automation of African mammal surveys but the developed approach still needs to be improved to manage double counting on entire transects. These results emphasize the importance of standardization of data acquisition, with strong spatial and temporal heterogeneities, in order to build robust models that can be used in similar environments and conditions
Counting African Mammal Herds in Aerial Imagery Using Deep Learning: Are Anchor-Based Algorithms the Most Suitable?
editorial reviewedMonitoring wildlife and livestock in protected areas is essential to reach natural ecosystem conservation goals. In large open areas, this is often carried out by direct counting from observers in manned aircrafts flying at low altitude. However, there are several biases associated with this method, resulting in a low accuracy of large groups counts. Unmanned Aerial Vehicles (UAVs) have experienced a significant growth in recent years and seem to be relatively well-suited systems for photographing animals. While UAVs allow for more accurate herd counts than traditional methods, identification and counting are usually indirectly done during a manual time-consuming photo-interpretation process. For several years, machine learning and deep learning techniques have been developed and now show encouraging results for automatic animal detection. Some of them use Convolutional Neural Networks (CNNs) through anchor-based object detectors. These algorithms automatically extract relevant features from images, produce thousands of anchors all over the image and eventually decide which ones actually contain an object. Counting and classification are then achieved by summing and classifying all the selected bounding boxes. While this approach worked well for isolated mammals or sparse herds, it showed limits in close-by individuals by generating too many false positives, resulting in overestimated counts in dense herds. This raises the question: are anchor-based algorithms the most suitable for counting large mammals in aerial imagery? In an attempt to answer this, we built a simple one stage point-based object detector on a dataset acquired over various African landscapes which contains six large mammal species: buffalo (Syncerus caffer), elephant (Loxodonta africana), kob (Kobus kob), topi (Damaliscus lunatus jimela), warthog (Phacochoerus africanus) and waterbuck (Kobus ellipsiprymnus). An adapted version of the CNN DLA-34 was trained on points only (center of the original bounding boxes), splat onto a Focal Inverse Distance Transform (FIDT) map regressed in a pixel-wise manner using the focal loss. During inference, local maxima were extracted from the predicted map to obtain the animals location. Binary model’s performances were then compared to those of the state-of-the-art model, Libra-RCNN. Although our model detected 5% fewer animals compared to the baseline, its precision doubled from 37% to 70%, reducing the number of false positives by one third without using any hard negative mining method. The results obtained also showed a clear increase in precision in close-by individuals areas, letting it appear that a point-based approach seems to be better adapted for animal detection in herds than anchor-based ones. Future work will apply this approach on other animal datasets with different acquisition conditions (e.g. oblique viewing angle, coarser resolution, denser herds) to evaluate its range of use
Surveying wildlife and livestock in Uganda with aerial cameras: Deep Learning reduces the workload of human interpretation by over 70%
peer reviewedAs the need to accurately monitor key-species populations grows amid increasing pressures on global biodiversity, the counting of large mammals in savannas has traditionally relied on the Systematic-Reconnaissance-Flight (SRF) technique using light aircrafts and human observers. However, this method has limitations, including non-systematic human errors. In recent years, the Oblique-Camera-Count (OCC) approach developed in East Africa has utilized cameras to capture high-resolution imagery replicating aircraft observers’ oblique view. Whilst demonstrating that human observers have missed many animals, OCC relies on labor-intensive human interpretation of thousands of images. This study explores the potential of Deep Learning (DL) to reduce the interpretation workload associated with OCC surveys. Using oblique aerial imagery of 2.1 hectares footprint collected during an SRF-OCC survey of Queen Elizabeth Protected Area in Uganda, a DL model (HerdNet) was trained and evaluated to detect and count 12 wildlife and livestock mammal species. The model’s performance was assessed both at the animal instance-based and image-based levels, achieving accurate detection performance (F1 score of 85%) in positive images (i.e. containing animals) and reducing manual interpretation workload by 74% on a realistic dataset showing less than 10% of positive images. However, it struggled to differentiate visually related species and overestimated animal counts due to false positives generated by landscape items resembling animals. These challenges may be addressed through improved training and verification processes. The results highlight DL’s potential to semi-automate processing of aerial survey wildlife imagery, reducing manual interpretation burden. By incorporating DL models into existing counting standards, future surveys may increase sampling efforts, improve accuracy, and enhance aerial survey safety
From crowd to herd counting: How to precisely detect and count African mammals using aerial imagery and deep learning?
peer reviewedRapid growth of human populations in sub-Saharan Africa has led to a simultaneous increase in the number of livestock, often leading to conflicts of use with wildlife in protected areas. To minimize these conflicts, and to meet both communities’ and conservation goals, it is therefore essential to monitor livestock density and their land use. This is usually done by conducting aerial surveys during which aerial images are taken for later counting. Although this approach appears to reduce counting bias, the manual processing of images is timeconsuming. The use of dense convolutional neural networks (CNNs) has emerged as a very promising avenue for processing such datasets. However, typical CNN architectures have detection limits for dense herds and closeby animals. To tackle this problem, this study introduces a new point-based CNN architecture, HerdNet, inspired by crowd counting. It was optimized on challenging oblique aerial images containing herds of camels (Camelus dromedarius), donkeys (Equus asinus), sheep (Ovis aries) and goats (Capra hircus), acquired over heterogeneous arid landscapes of the Ennedi reserve (Chad). This approach was compared to an anchor-based architecture, Faster-RCNN, and a density-based, adapted version of DLA-34 that is typically used in crowd counting. HerdNet achieved a global F1 score of 73.6 % on 24 megapixels images, with a root mean square error of 9.8 animals and at a processing speed of 3.6 s, outperforming the two baselines in terms of localization, counting and speed. It showed better proximity-invariant precision while maintaining equivalent recall to that of Faster-RCNN, thus demonstrating that it is the most suitable approach for detecting and counting large mammals at close range. The only limitation of HerdNet was the slightly weaker identification of species, with an average confusion rate approximately 4 % higher than that of Faster-RCNN. This study provides a new CNN architecture that could be used to develop an automatic livestock counting tool in aerial imagery. The reduced image analysis time could motivate more frequent flights, thus allowing a much finer monitoring of livestock and their land use
Molecular Diagnosis of Neonatal Diabetes Mellitus Using Next-Generation Sequencing of the Whole Exome
Background: Accurate molecular diagnosis of monogenic non-autoimmune neonatal diabetes mellitus (NDM) is critical for patient care, as patients carrying a mutation in KCNJ11 or ABCC8 can be treated by oral sulfonylurea drugs instead of insulin therapy. This diagnosis is currently based on Sanger sequencing of at least 42 PCR fragments from the KCNJ11, ABCC8, and INS genes. Here, we assessed the feasibility of using the next-generation whole exome sequencing (WES) for the NDM molecular diagnosis. Methodology/Principal Findings: We carried out WES for a patient presenting with permanent NDM, for whom mutations in KCNJ11, ABCC8 and INS and abnormalities in chromosome 6q24 had been previously excluded. A solution hybridization selection was performed to generate WES in 76 bp paired-end reads, by using two channels of the sequencing instrument. WES quality was assessed using a high-resolution oligonucleotide whole-genome genotyping array. From our WES with high-quality reads, we identified a novel non-synonymous mutation in ABCC8 (c.1455G.C/p.Q485H), despite a previous negative sequencing of this gene. This mutation, confirmed by Sanger sequencing, was not present in 348 controls and in the patient’s mother, father and young brother, all of whom are normoglycemic. Conclusions/Significance: WES identified a novel de novo ABCC8 mutation in a NDM patient. Compared to the current Sanger protocol, WES is a comprehensive, cost-efficient and rapid method to identify mutations in NDM patients. W
Two New Loci for Body-Weight Regulation Identified in a Joint Analysis of Genome-Wide Association Studies for Early-Onset Extreme Obesity in French and German Study Groups
Meta-analyses of population-based genome-wide association studies (GWAS) in adults have recently led to the detection of new genetic loci for obesity. Here we aimed to discover additional obesity loci in extremely obese children and adolescents. We also investigated if these results generalize by estimating the effects of these obesity loci in adults and in population-based samples including both children and adults. We jointly analysed two GWAS of 2,258 individuals and followed-up the best, according to lowest p-values, 44 single nucleotide polymorphisms (SNP) from 21 genomic regions in 3,141 individuals. After this DISCOVERY step, we explored if the findings derived from the extremely obese children and adolescents (10 SNPs from 5 genomic regions) generalized to (i) the population level and (ii) to adults by genotyping another 31,182 individuals (GENERALIZATION step). Apart from previously identified FTO, MC4R, and TMEM18, we detected two new loci for obesity: one in SDCCAG8 (serologically defined colon cancer antigen 8 gene; p = 1.85610 x 10(-8) in the DISCOVERY step) and one between TNKS (tankyrase, TRF1-interacting ankyrin-related ADP-ribose polymerase gene) and MSRA (methionine sulfoxide reductase A gene; p = 4.84 x 10(-7)), the latter finding being limited to children and adolescents as demonstrated in the GENERALIZATION step. The odds ratios for early-onset obesity were estimated at similar to 1.10 per risk allele for both loci. Interestingly, the TNKS/MSRA locus has recently been found to be associated with adult waist circumference. In summary, we have completed a meta-analysis of two GWAS which both focus on extremely obese children and adolescents and replicated our findings in a large followed-up data set. We observed that genetic variants in or near FTO, MC4R, TMEM18, SDCCAG8, and TNKS/MSRA were robustly associated with early-onset obesity. We conclude that the currently known major common variants related to obesity overlap to a substantial degree between children and adults
Post Genome-Wide Association Studies of Novel Genes Associated with Type 2 Diabetes Show Gene-Gene Interaction and High Predictive Value
Recently, several Genome Wide Association (GWA) studies in populations of European descent have identified and validated novel single nucleotide polymorphisms (SNPs), highly associated with type 2 diabetes (T2D). Our aims were to validate these markers in other European and non-European populations, then to assess their combined effect in a large French study comparing T2D and normal glucose tolerant (NGT) individuals. rs7903146 SNP, were combined (8.68-fold for the 14% of French individuals carrying 18 to 30 risk alleles with an allelic OR of 1.24). With an area under the ROC curve of 0.86, only 15 novel loci were necessary to discriminate French individuals susceptible to develop T2D. strongly associate with T2D in French individuals, and mostly in populations of Central European descent but not in Moroccan subjects. Genes expressed in the pancreas interact together and their combined effect dramatically increases the risk for T2D, opening avenues for the development of genetic prediction tests