Computer Vision for a Camel-Vehicle Collision Mitigation System

As the population grows and more land is being used for urbanization, ecosystems are disrupted by our roads and cars. This expansion of infrastructure cuts through wildlife territories, leading to many instances of Wildlife-Vehicle Collision (WVC). These instances of WVC are a global issue that is having a global socio-economic impact, resulting in billions of dollars in property damage and, at times, fatalities for vehicle occupants. In Saudi Arabia, this issue is similar, with instances of Camel-Vehicle Collision (CVC) being particularly deadly due to the large size of camels, which results in a 25% fatality rate [4]. The focus of this work is to test different object detection models on the task of detecting camels on the road. The Deep Learning (DL) object detection models used in the experiments are: CenterNet, EfficientDet, Faster R-CNN, and SSD. Results of the experiments show that CenterNet performed the best in terms of accuracy and was the most efficient in training. In the future, the plan is to expand on this work by developing a system to make countryside roads safer

Automated Leopard Alert And Reporting Mechanism Using Deep Learning

Today, rapid infrastructure development is taking place in major metropolitan cities, but unfortunately, this progress often involves the destruction of forest reserves, leaving wild animals homeless. The resulting environmental invasion forces these animals to venture into the cities, posing threats to citizens. In Mumbai, there have been numerous sightings of leopards and other wild animals near forested areas. Leopards have been known to attack street dogs, people, and vehicles, making it necessary to work on this problem. This paper suggests the utilization of deep learning models and object detection techniques to detect leopards and other potential threats. By integrating this technology with security applications, citizens can be made aware of the existence of wild animals in their vicinity. This research primarily focuses on addressing the concern of leopard sightings in Mumbai. The objective is to automate leopard detection and reporting using an object detection algorithm. In the proposed system, images of leopards are collected from an existing dataset available on Roboflow, comprising a total of 1000 samples. The proposed model's performance is evaluated using Mean Average Precision (mAP) & detection speed. The proposed method achieves an impressive mAP of 95.9% at a speed of 37 frames per second

Automatic sorting of Dwarf Minke Whale underwater images

Abstract: Apredictableaggregationofdwarfminkewhales(Balaenopteraacutorostratasubspecies) occurs annually in the Australian waters of the northern Great Barrier Reef in June–July, which has been the subject of a long-term photo-identification study. Researchers from the Minke Whale Project (MWP) at James Cook University collect large volumes of underwater digital imagery each season (e.g., 1.8TB in 2018), much of which is contributed by citizen scientists. Manual processing and analysis of this quantity of data had become infeasible, and Convolutional Neural Networks (CNNs) offered a potential solution. Our study sought to design and train a CNN that could detect whales from video footage in complex near-surface underwater surroundings and differentiate the whales from people, boats and recreational gear. We modified known classification CNNs to localise whales in video frames and digital still images. The required high classification accuracy was achieved by discovering an effective negative-labelling training technique. This resulted in a less than 1% false-positive classification rate and below 0.1% false-negative rate. The final operation-version CNN-pipeline processed all videos (with the interval of 10 frames) in approximately four days (running on two GPUs) delivering 1.95 million sorted images

Automatic non-biting midge (Chironomidae) identification through the application of object detection and deep learning techniques

This research study introduces a possible new method for the identification of chironomid larvae mounted on microscope slides in the form of an automatic computer-based identification tool using deep learning techniques. Deep learning is becoming an important tool for ecologists where there are advantages and limitations for its use as a rapid biomonitoring tool. Chironomids collected from the River Stour in Kent had their head capsules mounted on microscope slides and images of these were then captured using a Raspberry PI. Using these images, a series of object detection models were created to classify several different chironomid genera. These models were then used to show how different deep learning approaches, focusing on pre-training preparation, could improve the performance of image classification. The model comparisons included two object detection frameworks (Faster-RCNN and SDD frameworks), three balanced image sets (with and without augmentation) and variations of two hyperparameter values (Learning Rate and Intersection Over Union). All models were reported using the standard computer science object detection evaluation protocol, the mean average precision metric. Each model configuration was run three times,to allow for statistical significance evaluation. Additionally, a series of novel post training performance metrics were created examining a model’s prediction accuracy and its givenconfidence value in its prediction choice. The highest mean average precision value achieved was 0.751 by Faster-RCNN. The models highlighted significance between the two object detection frameworks, where the Faster-RCNN framework performed better than SDD framework; however, there was non-significance between the image sets and the hyperparameters values. All models produced similar accuracy results regardless of framework used (between 95.5%-97.7%), however, there were large differences between the confidence examinations, wherein Faster-RCNN produced more confident predictions than SSD. In conclusion, this investigation successfully developed object detection models using SSD and Faster-RCNN to classify between three chironomid genera. As a proof of concept, this study highlighted that automatic and rapid classification models using deep learning techniques can be applied for the correct taxonomic identification of difficult organisms, like chironomid larvae, further advancing the prospect of using this relatively new field of computer science for ecological research

Detección automática, clasificación y reconocimiento de escorpiones mediante técnicas de Aprendizaje Profundo

La detección e identificación temprana de los escorpiones es esencial debido a la peligrosidad de estos arácnidos que ponen en riesgo la salud de la población, en particular, de los sectores más vulnerables al veneno de un escorpión, como son las personas hipertensas, cardíacas o diabéticas, pero también los niños y los ancianos. A su vez, la detección y clasificación de escorpiones puede ser útil con fines de investigación biológica para estudiar las diferentes variedades de géneros y especies. En este trabajo, con el propósito de brindar herramientas de prevención alternativas, se desarrollaron novedosos sistemas automáticos y en tiempo real para detectar y clasificar escorpiones, utilizando heurísticas de visión artificial y Aprendizaje Profundo, basados en las características de la forma y la propiedad de fluorescencia de los escorpiones cuando son expuesto a luz ultravioleta. En particular, se han investigado las tres especies de escorpiones que se encuentran en la ciudad de La Plata: Bothriurus bonariensis (sin importancia sanitaria), Tityus carrilloi y Tityus confluens (ambas de importancia sanitaria). Durante este trabajo se llevaron a cabo comparaciones entre diferentes modelos basados en Aprendizaje Profundo utilizados para detectar e identificar escorpiones, ya sea por género peligroso o no peligroso, como para determinar su especie dentro de un mismo género. Los resultados satisfactorios obtenidos indican que los sistemas desarrollados pueden, de forma temprana, precisa, no invasiva y segura, detectar y clasificar escorpiones, incluso dentro de un ambiente no controlado, es decir, cuando el escorpión se encuentra cerca de otros objetos que podrían dificultar su detección. Los sistemas de detección y clasificación desarrollados en este trabajo se implementaron como una aplicación móvil, con la ventaja de la portabilidad y la facilidad de acceso a la población, que puede ser utilizada como una herramienta de prevención eficaz para minimizar las picaduras de escorpiones y ayudar a reducir el daño que pueden ocasionar a las poblaciones expuestas a estos arácnidos. Además, estos sistemas son fácilmente escalables a otros géneros y especies de escorpiones para ampliar la región donde se puedan utilizar estas aplicaciones.Facultad de Ingenierí