Automatic animal detection has become a critical capability in ecology, conservation, agriculture, and public safety, driven by the rapid growth of visual data collected through camera traps, UAVs, and remote sensors. The necessity of this study arises from the increasing demand to understand and apply these underlying detection techniques in practical domains such as animal husbandry, farming, and livestock management, where timely and accurate animal identification directly impacts productivity, welfare, and safety. Traditional convolutional neural networks (CNNs) have demonstrated strong accuracy in static or controlled environments but often face limitations in computational cost and inference speed. In contrast, the You Only Look Once (YOLO) family of one-stage detectors has revolutionized animal detection by achieving real-time performance while maintaining competitive accuracy across challenging geospatial environments. This review provides a chronological synthesis of detection approaches, tracing the evolution from handcrafted features and two-stage CNN-based models to modern YOLO architectures and transformer-enhanced frameworks. A detailed comparative analysis is presented, highlighting trade-offs in accuracy, speed, robustness, and deployment feasibility across diverse datasets, including camera trap imagery, UAV-based surveys, and satellite observations. Persistent challenges such as small-object detection, class imbalance, and limited cross-geographical generalization are discussed alongside enhancement strategies, including attention mechanisms, few-shot learning, and domain adaptation. Furthermore, practical deployment considerations are explored, with emphasis on edge computing platforms such as Jetson Nano, Coral TPU, and UAV-embedded systems. This review adopts a systematic methodology following PRISMA guidelines, covering studies published between 2015 and 2025, from which 142 were included after screening. Comparative findings show that on camera-trap datasets, transformer-augmented YOLO variants achieve up to 94% mAP under controlled illumination, while lightweight YOLOv7-SE and YOLOv8 architectures offer superior real-time performance (\(\ge\) 60 FPS) on UAV-based imagery. However, large-scale deployment remains constrained by edge-device memory limits and cross-domain generalization challenges.

Automated crack detection plays a vital role in the structural health monitoring of civil infrastructure, yet existing methods often remain limited to binary crack identification and are computationally demanding for real-time or edge deployment. This study presents a lightweight convolutional neural network, developed through the CNN-Block Development Mechanism (CNN-BDM), for multi-class crack and surface-type classification across six categories: cracked and uncracked concrete, plaster, and wall surfaces. The proposed framework integrates domain-driven data augmentation, balanced label design, and systematic regularization to achieve a compact yet high-performing model. Through iterative refinement, the final Lite-V2 architecture achieves a macro-F1 score of 0.928 and a test accuracy of 0.957 on the SDNET2018 dataset using only 0.28 million parameters. Cross-domain evaluations further validate the model’s generalization, attaining F1-scores of 0.975 on CrackForest (CFD) and 0.96 on DeepCrack. Grad-CAM visualizations confirm interpretable feature localization, while perturbation experiments under brightness and blur variations demonstrate robust resilience to real-world distortions. Comparative analysis against MobileNetV2, EfficientNet-B0, and ResNet-18 reveals that Lite-V2 delivers the highest accuracy and efficiency with up to 40 $$\times$$ fewer parameters and significantly reduced inference latency (11 ms) on a Raspberry Pi 4. These results establish Lite-V2 as an efficient, explainable, and deployment-ready framework for practical crack classification and condition monitoring in resource-constrained environments.

In recent years, several strategies have been introduced to enhance early warning systems and lower the risk of rock-falls. In this regard, this paper introduces a deep learning- and IoT-based framework for rock-fall early warning, devoted to reducing rock-fall risk with high accuracy. In this framework, the prediction accuracy was augmented by eliminating the uncertainties and confusion plaguing the prediction model. In order to achieve augmented prediction accuracy, this framework fused prediction model-based deep learning with a detection model-based Internet of Things. This study utilized parameters, namely, overall prediction performance measures based on a confusion matrix, to assess the performance of the framework in addition to its ability to reduce the risk. The result indicates an increase in prediction model accuracy from 86% to 98.8%. In addition, the framework reduced the risk probability from 1.51 × 10⁻³ to 8.57 × 10⁻⁹. Our findings demonstrate the high prediction accuracy of the framework, which also offers a reliable decision-making mechanism for providing early warning and reducing the potential hazards of rock falls.

During the last few years, several approaches have been proposed to improve early warning systems for reducing rock-fall risk. In this regard, this paper introduces a Deep learning-and (IoT) based Framework for Rock-fall Early Warning, devoted to reducing the rock-fall risk with high accuracy. In this framework, the prediction accuracy was augmented by eliminating the uncertainties and confusion plaguing the prediction model. In order to achieve augmented prediction accuracy, this framework fused the prediction model-based deep learning with a detection model-based Internet of Things. In order to determine the framework’s performance, this study adopted parameters, namely overall prediction performance measures, based on a confusion matrix and the ability to reduce the risk. The result indicates an increase in prediction model accuracy from 86% to 98.8%. In addition, a framework reduced the risk probability from (1.51 ×10-3) to (8.57 ×10-9). Our results indicate the framework’s high prediction accuracy; it also provides a robust decision-making process for delivering early warning and lowering the rock-fall risk probability.